GEOL157 lab 6: Attributing Climate Change

Introduction

In this lab we will answer the question of how much of recent warming can be blamed on human activities. As we saw in class, a key part of this logical argument rests upon model simulations of the climate of last 150 years. The model results are from the Coupled Model Intercomparison Project Phase 5 (CMIP5), which is one of the scientific bases for IPCC Fifth Assessment Report (2013). The idea is to subject climate models to different forcings and see the pattern and magnitude of their responses. Forcings include greenhouse gases, anthropogenic aerosols, solar insolation, volcanic activities, land use and also on. Forcings like greenhouse gases and anthropogenic aerosols are anthropogenic, while forcings like volcanic activities are natural. How do they stack up?


In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

Detection and Attribution in the CMIP5 ensemble

About 20 modelling groups in the world participate in CMIP5, and each modelling group may have a few climate models. For each experiment, models are not just run once. Multiple runs are done that differ only slightly in their starting point. Thus, differences between these runs illustrate the range of “natural” climate variability: that is, the depict many plausible histories that climate could have followed. One consequence of this is that you cannot expect a given simulation to match osbervations exactly; instead, we seek combinations of forcings and models that well-reproduce trends, or certain events (e.g. volcanic cooling).

We start by reading in global mean near-surface temperature time series forced by all forcings and natural forcings in CMIP5 models. The simulations with all forcings are part of the so-called "historical" experiment, which aimed to reproduce the climate over 1851-2005. Simulations exposed only to natural forcings are called "historicalNat" experiment. Here temperature is defined as anomalies relative the 1850-1880 average for each simulation.


In [2]:
# Temperature response by All forcings
df_all = pd.read_pickle('tas_ann_cmip5_historical.pkl')

In [3]:
df_all


Out[3]:
Year ACCESS1-0 ACCESS1-3 CCSM4 CESM1-BGC CESM1-CAM5 CMCC-CMS CNRM-CM5 CSIRO-Mk3-6-0 CanESM2 ... MIROC-ESM-CHEM MIROC5 MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M NorESM1-ME bcc-csm1-1 bcc-csm1-1-m inmcm4
0 1850 0.005912 -0.090138 0.000328 0.032450 0.041840 -0.024983 -0.038497 -0.105936 -0.006133 ... -0.023172 -0.042174 0.031862 0.074906 -0.098591 -0.038457 0.171138 -0.063076 -0.353140 -0.037016
1 1851 -0.081942 0.081735 0.081337 -0.049950 0.026731 0.196026 -0.059678 -0.033881 -0.004424 ... 0.101046 -0.033038 0.031071 0.053088 -0.059391 -0.091666 0.195029 -0.166249 -0.266049 -0.007625
2 1852 -0.081842 -0.003265 -0.025781 -0.216741 0.000412 0.020135 -0.018497 0.028810 0.035603 ... 0.049501 -0.048038 -0.014229 0.057443 -0.053328 0.017716 -0.020271 -0.120713 -0.319922 -0.067189
3 1853 0.119176 0.085608 0.020137 -0.105177 0.061067 -0.116801 0.126858 0.004928 0.053731 ... 0.137610 0.058180 -0.018820 0.064770 -0.035837 -0.114511 -0.142171 0.003242 -0.156840 0.011602
4 1854 0.140703 0.200480 -0.139636 -0.017277 -0.025097 -0.006719 -0.186660 0.014873 0.164294 ... 0.091219 -0.006702 -0.005611 0.073997 -0.052046 -0.093875 -0.034708 -0.068431 0.015642 -0.035816
5 1855 0.109676 -0.053574 -0.016299 0.109778 -0.103088 0.037190 -0.065115 0.037510 0.087994 ... -0.036772 -0.010502 -0.097284 0.101952 -0.053182 -0.060620 0.065174 -0.071149 -0.126767 -0.058080
6 1856 0.053167 0.011035 0.031328 -0.154813 -0.132788 -0.207247 0.040376 -0.018963 -0.077842 ... -0.011926 -0.012047 -0.092884 -0.051575 -0.166164 -0.166048 -0.076008 -0.102240 0.030551 -0.011789
7 1857 -0.022461 0.023153 -0.005263 -0.256022 -0.056415 -0.031438 -0.074397 -0.171254 -0.099024 ... -0.106517 -0.080592 -0.057066 -0.162203 -0.057428 0.017425 -0.050471 -0.426404 -0.325349 -0.044080
8 1858 -0.069161 -0.156874 -0.072536 -0.142895 -0.049469 0.066590 -0.050724 -0.137454 -0.088724 ... -0.047563 -0.071756 -0.050293 -0.080894 0.069882 0.033325 0.075283 -0.198722 -0.289140 -0.080044
9 1859 -0.129324 -0.122165 -0.067108 0.012832 -0.018160 -0.276092 -0.012697 -0.091281 -0.090497 ... 0.000010 -0.121992 -0.062593 -0.001357 0.013909 -0.044248 0.189556 -0.069158 -0.014131 -0.104307
10 1860 0.022976 -0.108929 -0.051172 0.092405 -0.087306 -0.004947 0.028303 -0.036099 -0.029151 ... -0.031299 -0.099074 -0.066438 0.027415 0.010291 -0.121457 0.043292 0.030296 -0.067558 -0.118262
11 1861 -0.028642 -0.061229 -0.038390 0.079868 -0.112115 0.243290 0.074694 -0.026018 0.024485 ... -0.057135 -0.037011 -0.026648 -0.092548 0.025772 -0.008066 -0.014980 -0.042240 -0.102403 -0.048935
12 1862 -0.064642 -0.125011 -0.027108 0.017505 -0.005297 -0.073056 -0.028124 -0.103890 -0.016906 ... -0.046172 0.027398 -0.070520 -0.017875 0.054672 0.010498 0.053202 0.034505 -0.038085 -0.042762
13 1863 0.047276 -0.087638 -0.000454 -0.058322 0.144176 0.017390 -0.049578 -0.095018 -0.080806 ... 0.019655 0.075971 -0.010566 0.006643 0.034809 -0.079648 0.009611 -0.219531 -0.022131 -0.068135
14 1864 0.049939 -0.063547 -0.035654 0.021350 0.030312 -0.114228 -0.023624 -0.095336 -0.122524 ... -0.078672 0.001326 -0.080529 -0.010421 0.018836 0.178171 -0.058562 -0.036658 0.091860 0.045920
15 1865 -0.055933 0.013071 -0.026408 0.012050 -0.018397 -0.093374 0.167822 -0.028136 -0.113569 ... -0.194472 -0.062411 -0.020084 0.003734 -0.012837 0.190271 -0.071862 -0.053413 -0.107894 0.078665
16 1866 -0.133479 0.031680 -0.047990 -0.027632 0.056294 0.270517 0.081776 0.016619 -0.072160 ... 0.048174 -0.037274 -0.028148 0.104734 -0.047082 0.058080 -0.079644 -0.090176 -0.063931 -0.060280
17 1867 0.015021 -0.081447 -0.002754 -0.036786 0.021522 0.091372 -0.047469 0.026228 -0.011969 ... 0.141992 -0.008411 0.116507 0.041670 -0.094800 0.012589 -0.079708 0.023251 0.053933 0.082538
18 1868 -0.041342 -0.131383 0.059055 0.133232 0.005894 0.032526 -0.046097 0.037892 -0.021633 ... 0.112283 0.045608 0.103134 -0.009839 -0.015355 0.124280 -0.035889 -0.004204 0.079378 0.122175
19 1869 0.125685 -0.012538 0.049701 0.092778 0.051703 0.025172 0.024258 0.019155 0.045185 ... -0.113290 -0.003574 0.018089 -0.055775 -0.046782 0.040216 0.067838 0.054405 0.095869 0.096929
20 1870 -0.014342 0.117689 -0.068708 -0.165722 0.077794 -0.060347 -0.069906 -0.008527 0.086612 ... -0.060626 -0.036983 0.038707 0.034734 0.047409 -0.025511 0.032147 0.116133 0.105369 0.078438
21 1871 0.031085 -0.051474 0.009182 0.020505 0.009531 0.043481 -0.100242 0.001228 -0.003978 ... 0.063901 0.033689 0.016152 0.025661 0.053418 -0.022484 0.089947 0.181824 0.116678 -0.056253
22 1872 -0.084242 -0.034174 0.044710 0.138614 0.046185 0.002662 0.045467 0.084855 0.077431 ... -0.043645 0.081662 -0.013229 -0.079430 0.083827 0.080316 0.062847 0.119051 0.150378 0.077493
23 1873 -0.025197 0.132971 0.095673 -0.131950 0.034267 -0.152738 -0.014624 0.111401 -0.014669 ... 0.065565 0.068171 0.016089 -0.057475 0.007663 0.019625 -0.206598 0.194160 0.196578 0.084020
24 1874 0.086276 0.062689 0.053173 -0.022622 -0.023824 -0.058483 0.066312 0.107728 0.066067 ... 0.053519 0.002780 0.097934 0.047925 0.133127 0.004980 -0.065289 0.161551 0.235588 0.029056
25 1875 0.008185 0.133044 0.084746 0.095905 0.003658 -0.081692 0.078958 0.102964 0.020212 ... -0.010572 0.102526 0.185116 0.069261 0.087291 -0.134575 0.017429 0.225396 0.141151 0.045084
26 1876 -0.046770 0.225162 0.098482 0.216459 -0.037688 -0.022856 -0.039824 0.082810 0.016203 ... -0.137017 0.172698 0.076752 -0.068212 0.102372 0.153298 -0.053053 0.145269 0.200542 0.167238
27 1877 -0.103788 0.040953 0.046419 0.152414 -0.020960 0.074772 0.020331 0.117646 0.069722 ... -0.012372 0.075826 0.054007 -0.031185 -0.119137 0.164825 0.049883 0.208760 0.197824 -0.003953
28 1878 0.088558 -0.027938 -0.057008 0.062250 0.020476 0.180017 0.129894 0.116328 0.065003 ... 0.105619 0.032017 0.001162 -0.067975 0.003318 -0.062138 0.010511 0.030469 0.216497 -0.048589
29 1879 0.079476 0.052053 0.008001 0.095514 0.058740 0.023862 0.040703 0.040819 0.041467 ... 0.021128 -0.066274 -0.071638 -0.001166 0.165363 -0.042311 -0.143671 0.204051 0.325497 -0.026044
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
131 1981 0.238403 0.108162 0.701337 0.882987 0.403131 0.121172 0.506685 0.278437 0.477703 ... 0.517846 0.337535 0.628243 0.540879 0.340045 0.257425 0.435838 0.600769 0.775388 0.511175
132 1982 0.210685 0.151089 0.611701 0.770387 0.349976 0.029426 0.398967 0.250601 0.375994 ... 0.373455 0.323253 0.609762 0.514143 0.362727 0.284334 0.438338 0.533742 0.628997 0.432093
133 1983 0.066739 0.236326 0.513346 0.423014 0.116867 0.055490 0.141403 0.115928 0.187985 ... 0.315965 0.143153 0.485898 0.359988 0.361472 0.284571 0.219765 0.468433 0.599706 0.565784
134 1984 0.139130 0.165489 0.598773 0.504296 0.158576 0.395162 0.320558 0.214219 0.246140 ... 0.379874 0.046998 0.586116 0.462434 0.398127 0.363007 0.359847 0.599433 0.606615 0.506575
135 1985 0.108776 0.212398 0.643446 0.544978 0.215512 0.455353 0.322776 0.263319 0.391422 ... 0.619855 0.086726 0.555825 0.501752 0.401072 0.405643 0.296065 0.596296 0.660106 0.542320
136 1986 0.093894 0.198962 0.711910 0.599359 0.292231 0.215808 0.544022 0.252082 0.445694 ... 0.509574 0.228244 0.586025 0.578670 0.195872 0.398443 0.244438 0.793224 0.766706 0.555920
137 1987 0.291394 0.321571 0.810492 0.605341 0.397040 0.096844 0.463785 0.323573 0.432867 ... 0.468946 0.383489 0.634171 0.625797 0.325627 0.461962 0.380092 0.791115 0.826251 0.556365
138 1988 0.318230 0.176680 0.859182 0.820968 0.343322 0.269153 0.511003 0.381410 0.579203 ... 0.632574 0.435980 0.601316 0.659697 0.281782 0.451862 0.453947 0.850460 0.827406 0.498275
139 1989 0.455085 0.257708 0.831055 0.807532 0.352449 0.409353 0.484158 0.403092 0.614494 ... 0.701765 0.419308 0.861798 0.719125 0.402163 0.611671 0.413174 0.925133 1.046833 0.517911
140 1990 0.425112 0.290035 0.909146 0.782514 0.335822 0.436008 0.699985 0.459728 0.639385 ... 0.692937 0.387253 0.957125 0.747497 0.416336 0.698998 0.623138 1.001515 1.012478 0.511511
141 1991 0.327676 0.379662 0.817646 0.663032 0.336031 0.526981 0.617085 0.441010 0.616931 ... 0.662592 0.362889 0.789698 0.758179 0.443291 0.427734 0.418202 1.000060 0.858542 0.491584
142 1992 0.067067 0.149880 0.524192 0.393941 0.267240 0.687453 0.189812 0.162728 0.381349 ... 0.295128 0.164871 0.440407 0.416670 0.138127 0.300771 0.168929 0.623042 0.558878 0.526184
143 1993 0.199112 0.181762 0.687664 0.532023 0.386385 0.640662 0.446949 0.220346 0.423385 ... 0.359928 0.357444 0.496325 0.542952 0.258236 0.378298 0.135738 0.750778 0.685824 0.490402
144 1994 0.360512 0.201298 0.753273 0.684823 0.342667 0.611753 0.670431 0.243437 0.479867 ... 0.327728 0.381417 0.652298 0.556997 0.156754 0.360543 0.245465 0.857515 0.786824 0.627138
145 1995 0.370839 0.234708 0.817128 0.853132 0.394576 0.418944 0.522622 0.358246 0.583522 ... 0.606492 0.319626 0.763943 0.659888 0.359791 0.355262 0.311683 0.811051 0.881069 0.607020
146 1996 0.441358 0.290680 0.892019 0.880187 0.420731 0.471108 0.518612 0.439610 0.752422 ... 0.733246 0.304471 0.813107 0.751961 0.418118 0.516780 0.291647 1.012969 1.112269 0.640875
147 1997 0.445348 0.301889 0.935864 1.088359 0.467967 0.743444 0.764394 0.456555 0.773367 ... 0.656337 0.509353 0.847034 0.937370 0.380600 0.497352 0.306538 1.052915 1.019224 0.579256
148 1998 0.549994 0.446962 1.051110 1.016923 0.586912 0.649935 0.671412 0.443973 0.826358 ... 0.721846 0.575189 0.903371 0.944015 0.415882 0.535143 0.420420 1.040769 1.258606 0.583084
149 1999 0.669203 0.364680 1.102637 1.099650 0.608022 0.484926 0.667149 0.566119 0.905731 ... 0.725937 0.575298 0.856798 0.908006 0.385018 0.706662 0.563192 1.069578 1.207333 0.631875
150 2000 0.676476 0.316262 1.130764 1.086141 0.566022 0.592535 0.822294 0.649564 0.950349 ... 0.658283 0.621562 0.897289 0.960215 0.458436 0.641843 0.719892 1.066542 1.169342 0.662120
151 2001 0.641921 0.437317 1.203801 1.008859 0.615131 0.892117 0.749912 0.660010 0.881376 ... 0.872710 0.686917 0.925562 0.986070 0.455045 0.609643 0.680374 1.108033 1.358797 0.751311
152 2002 0.666167 0.532662 1.221710 0.953759 0.740703 0.891644 0.864803 0.650864 0.978594 ... 0.754683 0.777798 0.934998 1.003888 0.500700 0.631289 0.732274 1.178224 1.491897 0.668747
153 2003 0.626739 0.510944 1.197773 1.031114 0.749967 0.587526 0.953749 0.644137 1.081003 ... 0.728919 0.764798 1.090543 0.995715 0.579009 0.742025 0.909483 1.109451 1.437342 0.619138
154 2004 0.808530 0.599808 1.230719 1.145787 0.726731 0.559408 0.992667 0.705155 1.085585 ... 0.811628 0.832544 1.154425 1.058670 0.577782 0.820352 0.960683 1.145524 1.449669 0.805729
155 2005 0.708767 0.662562 1.240437 1.363114 0.760994 0.711417 1.042403 0.754755 1.192094 ... 0.926774 0.785435 1.089552 1.026997 0.612627 0.806216 0.920411 1.167342 1.570442 0.704956
156 2006 0.710067 0.682008 1.260810 1.270278 0.810531 0.703944 0.979003 0.717592 1.243876 ... 0.949137 0.758671 1.141125 1.017961 0.515754 0.799962 0.807138 1.133878 1.470506 0.715356
157 2007 0.802921 0.668344 1.397292 1.228914 0.809931 0.807953 0.857576 0.697037 1.193876 ... 0.979755 0.801844 1.183934 1.122697 0.495954 0.667598 0.730883 1.292169 1.454878 0.787556
158 2008 0.779458 0.897198 1.360455 1.201087 0.809522 0.595235 1.125758 0.731292 1.154022 ... 1.173755 0.904335 1.138043 1.178743 0.566727 0.710552 0.738592 1.386951 1.444160 0.811811
159 2009 0.859494 0.912635 1.264546 1.311241 0.786712 0.734526 1.017222 0.773792 1.191785 ... 1.072001 0.923289 1.165771 1.177525 0.608436 0.673398 0.793120 1.346778 1.423142 0.778175
160 2010 0.947421 0.820380 1.277419 1.263623 0.813658 0.987399 0.953885 0.806364 1.271458 ... 1.125428 0.821953 1.177125 1.139179 0.580254 0.748071 0.790211 1.259142 1.272524 0.757993

161 rows × 33 columns

Question 0 How many simulations are there per model?

Answer 0:


In [4]:
# Temperature response by Natural forcings
df_nat = pd.read_pickle('tas_ann_cmip5_historicalNat.pkl')

In [5]:
df_nat


Out[5]:
Year BNU-ESM CCSM4 CESM1-CAM5-1-FV2 CNRM-CM5 CSIRO-Mk3-6-0 CanESM2 FGOALS-g2 GFDL-CM3 GFDL-ESM2M GISS-E2-H GISS-E2-R HadGEM2-ES IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM MIROC-ESM-CHEM MRI-CGCM3 NorESM1-M bcc-csm1-1
0 1850 -0.043956 0.047525 -0.128310 -0.097586 -0.073213 0.010673 0.070986 NaN NaN -0.017733 0.041652 NaN 0.012394 -0.033670 -0.018308 0.072031 -0.066430 -0.055138 -0.048620
1 1851 0.123698 0.002952 -0.085701 -0.068122 0.040596 0.146064 0.067041 NaN NaN -0.040952 0.073852 NaN 0.011312 -0.034343 0.013601 0.188085 -0.004275 0.127198 -0.010520
2 1852 -0.001129 -0.020802 0.088462 -0.013068 0.011132 0.132392 0.033477 NaN NaN -0.144006 0.154952 NaN -0.027552 -0.057679 -0.034236 0.214831 0.008597 0.166089 0.021989
3 1853 -0.096529 -0.026948 0.204062 -0.063004 -0.036149 0.066073 0.049559 NaN NaN -0.086624 0.023652 NaN -0.024024 -0.037552 0.027537 0.039512 -0.072130 -0.020029 0.087171
4 1854 -0.132502 -0.118921 -0.032283 -0.117977 -0.107613 0.103637 0.050432 NaN NaN -0.041824 0.067688 NaN -0.011897 -0.071088 0.016692 0.047876 -0.019294 0.002035 0.132608
5 1855 -0.080629 -0.065212 -0.120410 -0.079386 -0.025240 0.038546 -0.022932 NaN NaN 0.011258 0.092979 NaN -0.040906 -0.023615 0.157728 0.026185 -0.111403 0.135971 -0.082947
6 1856 0.032498 -0.051421 -0.021528 -0.068950 -0.046795 -0.036408 0.003068 NaN NaN -0.078806 0.056915 NaN -0.059424 -0.089334 -0.038790 0.081949 -0.067775 -0.068665 -0.062520
7 1857 0.000244 -0.086939 -0.080728 -0.074322 -0.171968 -0.049090 0.017232 NaN NaN -0.186842 -0.044185 NaN -0.049442 -0.160579 -0.117408 -0.031360 0.051652 -0.022702 -0.091302
8 1858 -0.143938 -0.043612 -0.060647 -0.048586 -0.123604 -0.129618 -0.024759 NaN NaN -0.198888 -0.019458 NaN -0.194842 -0.117679 -0.169154 0.003440 -0.083575 0.127644 -0.113774
9 1859 0.028580 0.032270 -0.117065 -0.016586 -0.082577 -0.117827 -0.026205 NaN NaN -0.045324 -0.029376 NaN -0.170424 -0.105779 -0.144663 -0.126188 -0.064248 0.120353 -0.046611
10 1860 0.181035 0.017807 0.036426 -0.011941 -0.104549 0.015992 0.004805 0.178287 NaN -0.045297 -0.053548 0.063500 -0.151142 -0.125925 -0.112108 -0.118133 -0.029885 0.153207 0.013453
11 1861 0.068862 -0.071630 0.077562 0.047032 -0.076595 -0.004172 0.048777 0.101941 0.094040 -0.029688 0.013679 0.129891 -0.055242 -0.061615 -0.077363 -0.110578 -0.051630 -0.030211 -0.026765
12 1862 -0.049683 -0.050239 -0.016801 0.030659 -0.063077 -0.080090 0.017414 0.104050 -0.070205 0.125348 -0.043585 0.129763 -0.040342 -0.010725 -0.132172 -0.156497 0.039834 0.016498 0.068408
13 1863 0.151289 0.051279 0.083090 -0.037622 -0.044940 -0.112899 0.008277 0.084223 0.097331 -0.032415 0.005288 0.167418 -0.098197 0.014457 -0.073554 -0.052715 0.052106 -0.075411 -0.079802
14 1864 0.105053 0.067607 0.144608 0.034959 -0.022268 -0.055227 -0.021832 -0.028895 0.170913 -0.012779 0.034815 0.086563 -0.008861 0.024694 0.031901 -0.098124 -0.004948 -0.138284 -0.038356
15 1865 -0.121538 -0.038639 0.131872 0.068123 0.013060 -0.012345 -0.065486 0.160014 0.139976 0.026539 0.015197 0.017382 0.009248 0.010875 0.051773 -0.021515 0.023143 -0.145556 -0.029356
16 1866 0.083426 -0.051839 -0.033919 0.004823 0.059969 -0.077272 -0.061623 0.178650 0.000395 0.152585 0.046133 -0.012473 0.047458 0.049303 0.051382 -0.096024 0.047297 -0.192756 0.012544
17 1867 -0.033774 0.067579 -0.014574 0.005868 -0.004295 -0.058763 -0.031341 0.066987 -0.055415 0.082712 -0.089212 0.040027 0.019430 0.071475 0.024773 -0.110733 -0.003266 -0.089211 -0.062211
18 1868 -0.364674 -0.001848 0.168099 0.014378 0.007442 0.054155 -0.019723 0.121932 -0.115605 0.071876 0.062315 0.043782 0.020148 0.005612 -0.034981 0.047403 -0.028012 0.019435 0.004626
19 1869 -0.111347 -0.027584 0.115644 0.017723 0.063742 -0.006308 0.007886 0.060568 0.134222 0.085303 0.075461 0.070754 0.033630 -0.002815 0.031473 0.012794 0.008770 -0.110629 0.006380
20 1870 0.128008 0.000234 -0.065101 0.062132 0.067851 0.046619 -0.036250 0.119759 0.172995 0.064703 0.039942 0.132200 0.123467 0.111530 0.085855 0.066676 0.113479 -0.040020 0.095835
21 1871 -0.128311 0.013598 -0.069192 0.059050 0.073978 0.029237 -0.057395 0.165623 0.043776 0.084867 -0.099176 0.161445 0.106848 0.076948 0.139792 0.118603 -0.007885 0.043416 -0.020183
22 1872 0.043380 -0.053693 0.034435 0.064232 0.103614 0.103792 -0.071050 0.110505 -0.104015 0.033848 -0.054094 0.142582 0.151303 0.081039 0.174101 0.028349 0.000488 0.152998 0.037508
23 1873 0.015553 0.028261 0.046126 0.014959 0.160614 0.074773 -0.020323 0.050778 0.233631 0.015094 0.015942 0.159791 0.058021 0.045348 0.175073 0.122940 0.141761 0.256462 0.082171
24 1874 -0.035029 0.059052 -0.041710 0.016268 0.105578 0.001964 -0.051177 0.142350 0.318831 0.024148 0.007442 0.107636 0.045530 0.105594 0.065282 0.081122 0.123915 -0.024556 -0.008347
25 1875 0.023008 0.046561 0.087053 0.072123 0.057523 0.004155 0.019105 0.113805 0.000531 0.056076 -0.034130 0.084282 0.029858 0.090912 -0.021808 -0.003942 0.129088 -0.092593 -0.018811
26 1876 0.006244 0.055943 0.124990 0.026159 0.038760 -0.019472 0.051232 -0.069086 0.076385 0.055121 -0.097258 -0.033173 0.082948 0.044366 -0.072718 -0.038906 0.115797 -0.163629 0.131162
27 1877 0.380953 0.094625 -0.111556 0.068441 0.064723 -0.092045 0.055123 0.038650 0.068831 0.041894 -0.122812 -0.027146 0.111812 0.095712 -0.034454 0.036231 -0.082812 0.023935 0.035889
28 1878 0.094326 0.083425 -0.158138 0.069096 0.053678 0.001046 0.000332 0.062596 0.037722 0.013067 -0.066112 0.003400 0.054967 0.055275 -0.019281 -0.059597 -0.146103 -0.083247 0.076271
29 1879 -0.123120 0.040607 -0.184765 0.021123 0.060623 0.022419 0.005350 0.077696 0.137213 0.016739 -0.074958 0.018463 0.013921 0.049257 0.054037 -0.163715 -0.012257 0.007398 -0.065892
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
131 1981 -0.042529 0.015116 0.010853 0.051796 0.046551 0.014855 0.047968 0.195359 0.007713 0.063230 -0.027512 0.115418 0.063539 -0.063215 0.061992 0.200312 0.009334 0.055316 0.065726
132 1982 -0.000074 -0.130757 -0.103819 -0.114786 0.031287 -0.146199 0.023595 -0.016395 0.031085 0.026039 -0.214748 0.062591 -0.079906 -0.146706 -0.059081 0.160812 -0.080530 -0.054947 -0.169938
133 1983 -0.284092 -0.348457 -0.149965 -0.273713 -0.187613 -0.161772 -0.046141 -0.308295 -0.119742 -0.193624 -0.296312 -0.096509 -0.128570 -0.113697 -0.113181 0.024758 -0.044957 -0.083047 -0.242665
134 1984 -0.063692 -0.276521 -0.083965 -0.176213 -0.022640 -0.200654 -0.079895 -0.173141 -0.241560 -0.130042 -0.223985 -0.036155 -0.081697 -0.046425 -0.052327 0.030922 0.105206 -0.097075 -0.140702
135 1985 -0.121720 -0.173039 -0.068101 -0.119913 0.041551 -0.131345 -0.065150 -0.105641 -0.190178 -0.009224 -0.239612 0.022272 -0.002915 -0.051697 0.021001 -0.036851 0.077370 -0.024738 -0.054902
136 1986 0.073426 -0.185821 0.012481 -0.116168 -0.076740 -0.078872 -0.007268 0.021432 -0.202496 0.018176 -0.170921 0.019082 -0.079097 -0.093706 0.029192 -0.133688 0.029534 0.035189 -0.016465
137 1987 0.116171 -0.146348 0.045062 -0.055577 -0.051377 -0.116518 0.017477 0.074314 -0.201605 0.017276 -0.108276 0.023163 -0.056952 -0.021315 0.080973 -0.071924 0.066834 0.140362 -0.121774
138 1988 0.018326 -0.104866 -0.126028 -0.021486 0.018287 -0.071718 -0.035750 0.112641 -0.113342 0.058921 0.049206 0.047227 0.010558 0.006185 0.138619 -0.105478 0.009697 0.116653 0.018308
139 1989 0.310453 -0.031912 -0.073465 0.025496 0.027651 -0.091181 -0.090786 0.116605 0.024295 0.011885 0.035442 0.106782 0.116021 -0.059652 0.227637 -0.031788 -0.021321 0.059307 -0.054783
140 1990 0.138017 -0.010357 0.018762 -0.025750 0.053478 -0.106936 -0.122341 0.038841 -0.030705 0.024121 0.028633 0.076372 0.117330 0.025630 0.194082 0.014203 -0.071030 -0.046575 0.016062
141 1991 -0.259856 -0.162130 -0.159674 -0.163941 -0.015531 -0.116136 -0.007405 0.038878 -0.089215 -0.024924 -0.080394 0.038245 -0.042597 -0.092088 0.105419 -0.110678 -0.135221 -0.040556 -0.065547
142 1992 -0.550383 -0.475212 -0.381665 -0.440941 -0.317540 -0.419581 -0.021359 -0.367459 -0.329615 -0.359770 -0.365939 -0.204228 -0.247615 -0.263088 -0.178027 -0.380078 -0.332203 -0.219711 -0.450292
143 1993 -0.138338 -0.338521 -0.262310 -0.371922 -0.247686 -0.363354 -0.020332 -0.427604 -0.275369 -0.240861 -0.386203 -0.133446 -0.227152 -0.335479 -0.135645 -0.241315 -0.167139 -0.309211 -0.372483
144 1994 0.090489 -0.225775 -0.165138 -0.227886 -0.080713 -0.396590 -0.016577 -0.176568 -0.320733 -0.178733 -0.166430 -0.082682 -0.157670 -0.258915 -0.019872 -0.163033 -0.283885 -0.081129 -0.198838
145 1995 -0.170838 -0.209712 -0.209901 -0.195032 -0.022986 -0.291963 -0.049395 -0.002404 -0.165042 -0.107170 -0.043194 -0.078946 -0.095270 -0.001679 -0.064099 -0.034042 -0.183357 0.108380 -0.163620
146 1996 0.053753 -0.186639 -0.084174 -0.133241 0.000442 -0.159818 -0.015286 -0.009377 -0.170133 -0.108733 0.009961 -0.090937 0.001121 -0.076515 0.042982 -0.046569 -0.206830 -0.028538 -0.102374
147 1997 0.076535 -0.108221 0.028844 -0.170686 0.056514 -0.160536 -0.005941 0.023823 -0.078251 -0.043124 0.078542 -0.044528 0.027739 -0.025643 0.117228 -0.026542 -0.006912 0.112271 -0.082165
148 1998 0.005135 -0.086284 0.133444 -0.104232 0.107787 -0.239527 -0.048695 0.041396 -0.190996 -0.003070 -0.077994 -0.080328 0.030030 0.017648 0.202810 0.009712 0.113988 0.220716 -0.080120
149 1999 -0.036392 -0.069348 -0.036710 -0.105822 0.145387 -0.110727 -0.006405 0.097514 -0.055615 0.022267 0.063488 -0.079437 0.044403 0.001330 0.139355 -0.002651 -0.020775 0.111135 -0.003383
150 2000 0.008562 -0.063993 -0.132356 -0.087077 0.115787 -0.061890 0.058677 0.160278 0.105276 0.065658 0.033261 0.034472 -0.002179 0.098085 0.185419 0.195758 -0.040830 0.039253 0.076426
151 2001 -0.032120 -0.054812 -0.111938 -0.063341 0.146042 -0.004572 0.048286 0.246759 -0.043778 0.021903 -0.030458 0.151472 0.155294 0.069421 0.157701 0.350531 -0.004021 -0.000047 0.031162
152 2002 -0.124383 -0.029639 -0.087256 -0.054604 0.215296 -0.009490 -0.044595 0.170696 -0.087705 -0.019115 -0.020594 0.108845 0.205003 0.186175 0.109437 0.185676 0.147706 0.125080 0.039144
153 2003 0.143853 -0.054157 -0.026474 -0.008704 0.173942 0.004464 -0.046905 0.229223 0.066049 0.005967 0.065697 0.180372 0.122394 0.147821 0.155464 0.150394 0.156225 -0.011711 0.059698
154 2004 0.139308 -0.025402 0.064572 0.004259 0.123114 -0.124636 -0.059059 0.205341 0.253495 0.031130 0.135542 0.119554 0.108839 0.068239 0.167292 0.258785 0.136606 -0.067975 0.099989
155 2005 0.067462 0.045598 -0.063183 0.009768 0.142423 -0.131918 -0.000323 0.190414 0.210295 0.149248 0.179315 0.079918 0.162276 0.060885 0.162519 0.221985 0.015734 -0.047829 0.149726
156 2006 NaN NaN NaN 0.070587 0.191487 -0.060145 -0.052395 NaN NaN 0.080585 0.179388 0.135654 0.195439 0.059239 NaN NaN NaN -0.180175 0.037508
157 2007 NaN NaN NaN -0.036068 0.175905 0.024692 -0.071268 NaN NaN 0.007976 0.178588 0.139336 0.160967 -0.056261 NaN NaN NaN -0.028029 0.088398
158 2008 NaN NaN NaN -0.007595 0.119642 0.019637 -0.033114 NaN NaN 0.120521 0.113833 0.134854 0.063585 -0.092825 NaN NaN NaN 0.069644 0.054289
159 2009 NaN NaN NaN 0.033950 0.115578 -0.011299 -0.048823 NaN NaN 0.015058 0.022897 0.156582 0.057976 -0.005297 NaN NaN NaN 0.037089 0.034280
160 2010 NaN NaN NaN 0.010941 0.113642 0.001355 NaN NaN NaN 0.051694 0.130970 0.123109 0.059958 0.047421 NaN NaN NaN 0.058771 0.098808

161 rows × 20 columns

Now let's load instrumentally observed temperature datasets to compare with the models


In [6]:
# observations
# HadCRUT4 global surface temperature
df_Had = pd.read_csv('HadCRUT.4.6.0.0.annual_ns_avg.txt',delim_whitespace=True,header=None)
# GISTEMP global surface temperature
df_GIST = pd.read_csv('GLB.Ts+dSST.csv',delimiter=',',skiprows=1, na_values='***')

Let's plot both sets of simulations, as well as the observations


In [7]:
plt.style.use('ggplot')

# All forcings
# median of models
line1,=plt.plot(df_all['Year'],df_all.iloc[:,1:].median(axis=1),linewidth=3,color='C0',label='All forcings')
# time series of each model with thin lines
for i in np.arange(df_all.shape[1]-1):
    plt.plot(df_all['Year'],df_all.iloc[:,i+1],linewidth=0.5,color='C0',alpha=0.5)

# Natural forcings
# median of models
line2,=plt.plot(df_nat['Year'],df_nat.iloc[:,1:].median(axis=1),linewidth=3,color='C1',label='Natural forcings only')
# time series of each model with thin lines
for i in np.arange(df_nat.shape[1]-1):
    plt.plot(df_nat['Year'],df_nat.iloc[:,i+1],linewidth=0.5,color='C1',alpha=0.5)

# observations, with black lines
df_Had_ano = df_Had[1]-df_Had[1][0:30].mean()
#line3,=plt.plot(df_Had[0][0:161],df_Had_ano[0:161],label='observations',linewidth=1,color='black')
df_GIST_ano = df_GIST['J-D']-df_GIST['J-D'][0:30].mean()
line4,=plt.plot(df_GIST['Year'][0:131],df_GIST_ano[0:131],label='GISTEMP',linewidth=1,color='black')

plt.legend(handles=[line1,line2,line4],loc="upper left")
plt.xlabel('Year')
plt.ylabel('Temperature anomaly (K)')


Out[7]:
Text(0,0.5,'Temperature anomaly (K)')

The range spanned by the wiggly lines gives a measure of ther interal variability of the models. That is. for a given forcing in a given year, these lines are meant to protray the range of possible climate states. To simplify the display, we can also show this as 90% central quantiles (i.e. spanning 5% to 95% of the data).


In [8]:
# All forcings
plt.plot(df_all['Year'],df_all.iloc[:,1:].median(axis=1),linewidth=3,color='C0',label='All forcings')
plt.fill_between(df_all['Year'],df_all.iloc[:,1:].quantile(0.05,axis=1),df_all.iloc[:,1:].quantile(0.95,axis=1),label='All forcing 90% CI',alpha=0.3)
# Natural forcings
plt.plot(df_nat['Year'],df_nat.iloc[:,1:].median(axis=1),linewidth=3,color='C1',label='Natural forcings only')
plt.fill_between(df_nat['Year'],df_nat.iloc[:,1:].quantile(0.05,axis=1),df_nat.iloc[:,1:].quantile(0.95,axis=1),label='Natural forcing 90% CI',alpha=0.3)

# observations
#line3,=plt.plot(df_Had[0][0:161],df_Had_ano[0:161],label='HadCRUT4',linewidth=1,color='black')
line4,=plt.plot(df_GIST['Year'][0:131],df_GIST_ano[0:131],label='GISTEMP',linewidth=1,color='black')

plt.legend(loc='upper left')
plt.xlabel('Year')
plt.ylabel('Temperature anomaly')


Out[8]:
Text(0,0.5,'Temperature anomaly')

Question 1: Which experiment is more consistent with observations? Based on this, will you attribute the climate change to natural forcings or anthropogenic forcings?

Answer 1:

Question 2: When answering Question 1, what's your standard of "consistent" when comparing curves?

Answer 2:

Question 3: The decrease of temperature in the early 1990s corresponds to a very large volcanic eruption event. What mechanism do you think can explain that the volcanic eruption causes global temperature decrease?

Answer 3:

An important question is: "When did man-made climate change emerged from the background of natural variability, both internally-genrerated (e.g. El Niño) and externally-forced?". Let us compare the probability distribution of temperature anomalies in these two experiments year by year and try to find when the probability distributions of these cease to overlap.

First let's calculate the Kernel Density Function of temperature anomaly in each year.


In [9]:
# Kernel Density Function
kde_all = {}
kde_nat = {}

In [10]:
for i in np.arange(1850,2011):
    kde_all[i] = {}
    ax=df_all.iloc[i-1850,1:].plot.kde()
    kde_all[i]['x']=ax.get_children()[i-1850]._x
    kde_all[i]['y']=ax.get_children()[i-1850]._y



In [11]:
for i in np.arange(1850,2011):
    kde_nat[i] = {}

    ax=df_nat.iloc[i-1850,1:].plot.kde()
    kde_nat[i]['x']=ax.get_children()[i-1850]._x
    kde_nat[i]['y']=ax.get_children()[i-1850]._y



In [12]:
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display

In [13]:
def pltkde(year):
    plt.plot(kde_all[year]['x'],kde_all[year]['y'],label='All forcings')
    plt.plot(kde_nat[year]['x'],kde_nat[year]['y'],label='Natural forcings')
    plt.xlabel('Temperature anomaly')
    plt.ylabel('Probability')
    plt.legend()

Please run these commands in your teminal if the slider doesn't show:

  • pip install ipywidgets

  • jupyter nbextension enable --py --sys-prefix widgetsnbextension


In [14]:
interact(pltkde, year=(1850, 2010, 1))


Out[14]:
<function __main__.pltkde>

Question 4: Please change the year in the slider, when do you think temperature response in the "all forcings" experiment seperates from that in the "natural forcings" experiment?

Answer 4:

Each category (natural or anthropogenic) includes several kinds, such as greenhouse gases, anthropogenic aerosols, solar irradiance, and volcanic activity. By using climate models, we can seperate the contribution of each forcing to climate change. Here we will analyze the model output from the GISS model [Marvel et al. (2016)] to see the contribution of different forcings.

Let's read the response of ocean heat content by different forcings. In Marvel et al. (2016), to consider the internal variability in the model, multiple runs are done that differ only slightly in their starting point for each forcing experiment. They are indexed R1, R2, etc


In [15]:
ohc = pd.read_csv('ohc.Marvel_etal2015.csv')

In [16]:
ohc


Out[16]:
Year All forcings Ensemble Mean All forcings R1 All forcings R2 All forcings R3 All forcings R4 All forcings R5 All forcings R6 Anthropogenic tropospheric aerosol Ensemble Mean Anthropogenic tropospheric aerosol R1 ... Solar R2 Solar R3 Solar R4 Solar R5 Volcanic Ensemble Mean Volcanic R1 Volcanic R2 Volcanic R3 Volcanic R4 Volcanic R5
0 1850 0.595979 1.033132 0.948472 1.245966 0.086574 -0.684885 0.946614 0.381123 0.919550 ... 0.996126 1.474749 -0.012580 -0.661525 0.559404 1.169655 1.057983 1.316933 0.124255 -0.871806
1 1851 0.724605 0.917449 1.092046 1.209630 0.290928 -0.256098 1.093674 0.311976 0.617146 ... 1.107255 1.576754 -0.101009 -0.425750 0.813444 1.159514 1.412848 1.754281 0.704800 -0.964224
2 1852 0.850923 0.947071 1.149636 1.213727 0.713551 -0.244096 1.325649 0.019951 0.271089 ... 1.099878 1.209883 0.264963 -0.717634 0.912322 0.782424 1.833514 1.926378 1.028545 -1.009254
3 1853 1.119904 0.942387 1.913577 1.632057 0.924815 -0.212759 1.519346 0.027390 0.160585 ... 1.283580 1.402812 0.630441 -1.047488 0.964464 0.371606 1.978686 2.049416 0.934913 -0.512298
4 1854 1.346502 0.920624 2.134961 1.673247 1.407607 -0.043562 1.986132 0.017271 0.310807 ... 1.013352 1.017732 1.009690 -1.274217 1.132118 0.676498 1.723336 2.481678 1.012228 -0.233151
5 1855 1.465347 0.823582 2.495141 1.525199 1.930659 0.041043 1.976459 -0.001526 0.163738 ... 0.944504 0.756415 1.192716 -1.450653 1.371950 1.312346 1.782448 2.683812 1.055901 0.025245
6 1856 1.265596 0.377425 2.228259 1.032064 1.700005 0.463278 1.792546 -0.036811 0.001997 ... 1.130221 0.898011 1.126720 -1.212441 1.158298 0.663147 1.471572 2.186917 1.196114 0.273740
7 1857 0.362296 -0.566428 1.282317 0.297041 0.920902 -0.542125 0.782066 -0.058569 -0.325708 ... 1.334244 0.944272 1.160046 -1.199410 0.166017 -0.610994 0.321966 1.209844 0.489525 -0.580258
8 1858 -0.105018 -0.999640 0.425850 -0.058483 0.764912 -1.245711 0.482964 -0.175956 -0.661579 ... 1.324945 0.978124 1.526568 -0.918760 -0.478180 -1.380014 -0.400531 0.505067 0.003525 -1.118946
9 1859 -0.053146 -0.956471 0.634904 -0.557407 1.276617 -1.241151 0.524632 -0.284213 -0.501958 ... 1.227867 0.941425 1.732684 -0.923391 -0.528851 -1.617496 -0.115960 0.327730 -0.147876 -1.090655
10 1860 0.141585 -0.946441 0.787993 -0.499757 2.005290 -1.053962 0.556385 -0.325133 -0.533232 ... 1.708275 1.082552 1.509381 -0.786448 -0.112082 -1.651250 0.391689 1.159968 0.217755 -0.678572
11 1861 0.391921 -0.811248 1.155393 -0.484708 2.571745 -1.059474 0.979818 -0.266775 -0.605742 ... 1.670353 1.088934 1.182457 -0.771389 0.103021 -1.489138 0.592019 1.593542 0.390649 -0.571967
12 1862 0.553104 -0.775634 1.427801 -0.295855 2.605149 -0.765592 1.122753 -0.476817 -0.915277 ... 1.288716 1.099146 1.349090 -0.675013 0.089079 -1.366128 0.461785 1.530667 0.449954 -0.630881
13 1863 0.561382 -0.671725 1.936445 -0.411463 2.471488 -0.844464 0.888010 -0.373885 -1.245246 ... 1.496739 0.615963 1.794503 -0.673819 0.110133 -1.593183 0.442962 1.403419 0.422100 -0.124633
14 1864 0.724719 -0.430921 2.023642 -0.183254 2.626855 -0.534955 0.846947 -0.275810 -1.297657 ... 1.348047 0.653546 1.816785 -0.663820 0.215499 -1.420261 0.647812 1.046389 0.830930 -0.027375
15 1865 0.814712 -0.394426 1.902706 -0.489677 3.133868 -0.490213 1.226015 -0.249729 -1.291022 ... 1.054765 0.886704 1.735827 -0.169210 0.283796 -1.017908 0.671309 1.231931 0.650014 -0.116367
16 1866 0.829339 -0.406315 1.858805 -0.599059 3.072637 -0.370426 1.420391 -0.195399 -1.197483 ... 0.625018 0.285741 2.051151 -0.084702 0.454349 -0.895235 0.964511 1.470798 0.771661 -0.039990
17 1867 1.001222 -0.550805 1.992021 -0.240487 3.406705 -0.416313 1.816211 -0.209676 -1.346339 ... 0.388247 0.094156 2.422207 -0.318445 0.625804 -0.862513 1.409958 1.678110 0.800965 0.102500
18 1868 1.132920 0.033235 2.169260 -0.504920 3.771220 -0.538662 1.867384 -0.302574 -1.741708 ... 0.253881 0.297680 2.560905 -0.194315 0.792745 -0.661221 1.590155 1.957882 0.827167 0.249741
19 1869 1.288302 0.263953 2.453737 -0.259396 4.263649 -0.536134 1.544004 -0.308065 -1.599937 ... 0.014795 0.251333 2.407329 0.008736 0.931556 -0.407664 1.678323 2.229851 0.588065 0.569207
20 1870 1.363521 0.268165 2.469499 -0.107213 4.270315 -0.431483 1.711841 -0.262720 -1.610305 ... 0.336713 0.004493 2.439963 0.014762 1.046807 0.017604 1.570441 2.258974 0.492289 0.894728
21 1871 1.655477 0.221160 3.163789 0.001230 4.493101 -0.234073 2.287654 -0.332093 -1.958486 ... 0.295236 0.097304 2.457485 0.172349 1.243579 0.015094 1.991560 2.105400 0.911350 1.194491
22 1872 2.065206 0.726086 3.692354 0.668760 4.919018 -0.473839 2.858856 -0.283597 -2.001845 ... 0.283862 -0.089640 2.192715 0.448532 1.391549 -0.182974 2.461607 2.141116 1.039346 1.498649
23 1873 2.154275 0.781015 3.934220 0.877826 4.667494 -0.317732 2.982824 -0.384275 -2.151266 ... 0.058452 -0.075455 2.179602 0.524929 1.456086 -0.358908 2.639857 2.578741 0.716496 1.704245
24 1874 2.408178 0.914479 4.149438 1.302173 5.079289 0.131112 2.872575 -0.512325 -2.040668 ... 0.328201 -0.508915 1.912076 1.055435 1.658381 -0.733101 2.730707 2.941955 0.696812 2.655532
25 1875 2.628405 0.842270 4.679478 1.490480 5.387366 0.633881 2.736953 -0.514830 -2.231236 ... 0.377865 -0.835206 1.542017 1.191327 1.808347 -0.353467 2.774721 3.091218 0.247315 3.281946
26 1876 2.827188 1.247794 4.928034 1.705900 5.090939 0.959442 3.031020 -0.419968 -2.268077 ... -0.030286 -1.037058 1.472916 1.521948 1.865737 0.016211 3.023546 2.989433 -0.015023 3.314519
27 1877 2.967591 1.494406 5.129058 1.562753 5.314494 1.206666 3.098166 -0.528715 -2.450163 ... -0.316344 -1.092911 1.197427 1.419764 1.886487 0.076216 3.168346 2.975806 -0.434202 3.646269
28 1878 3.037198 1.339996 5.383632 1.718256 5.470967 1.232833 3.077502 -0.473152 -2.584200 ... -0.105619 -1.110589 1.171628 1.929357 2.035484 0.137116 3.693231 2.912095 -0.623945 4.058920
29 1879 3.323603 1.624920 5.253866 1.967221 5.886041 1.548608 3.660962 -0.469376 -2.944271 ... -0.114171 -1.108898 1.152465 2.246123 2.392789 0.635391 3.937061 3.264438 -0.258933 4.385990
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
126 1976 33.841553 29.173316 36.586296 36.681772 35.209734 37.022605 28.375592 -18.509766 -21.383579 ... 0.463326 -3.639937 -0.223262 -1.804152 3.984429 3.585196 7.912046 3.628491 3.094885 1.701529
127 1977 34.574762 29.908651 37.293459 37.794785 35.676020 37.724773 29.050882 -19.097589 -21.887622 ... -0.266077 -3.979504 -0.223781 -1.566390 4.007641 3.829751 7.881792 3.505407 2.838982 1.982276
128 1978 35.475441 31.009521 38.170295 38.864643 36.381318 38.315026 30.111843 -19.711671 -22.829472 ... -0.389686 -3.949829 -0.091432 -1.824567 4.174624 3.913621 7.812665 4.006445 3.044135 2.096253
129 1979 36.271494 31.902438 39.381891 39.236149 37.111759 39.004171 30.992557 -20.369803 -23.760352 ... -0.587661 -3.999338 0.190972 -1.804901 4.081951 3.385056 8.032755 4.271212 2.875073 1.845660
130 1980 37.308667 32.867362 40.364006 40.355764 38.248517 39.951730 32.064622 -20.957427 -24.252611 ... -0.397159 -3.722264 0.182641 -1.416857 4.178108 3.172283 7.853636 4.829951 3.110399 1.924269
131 1981 38.342857 33.952459 41.297695 41.491017 39.508841 40.632246 33.174887 -21.282915 -24.202918 ... -0.772952 -3.548315 -0.068433 -1.235954 4.473148 3.652870 8.516607 4.979138 2.975399 2.241726
132 1982 38.990557 34.474482 42.152700 42.208775 40.216758 41.333427 33.557202 -21.740359 -24.804290 ... -1.047477 -3.444599 0.395464 -1.003952 4.200222 3.171120 8.372292 4.838957 2.529003 2.089738
133 1983 38.431350 33.803174 41.374715 41.831077 39.878889 40.822832 32.877413 -22.461504 -25.671766 ... -1.054996 -3.779614 0.687536 -1.173051 2.897854 1.751315 7.217683 3.360811 1.326237 0.833225
134 1984 38.862928 34.426901 41.956509 42.119160 40.310020 40.847702 33.517274 -23.140453 -26.244523 ... -1.030278 -4.089847 0.724148 -0.990232 2.403300 1.092898 6.949260 3.045073 0.515799 0.413468
135 1985 39.846297 35.425643 43.119339 42.895712 41.495934 41.630582 34.510571 -23.711101 -26.613202 ... -0.899762 -4.052640 0.266098 -0.671515 2.454898 0.936513 6.784779 3.309764 0.623352 0.620082
136 1986 40.814501 36.367044 43.929037 43.964290 42.660358 42.593369 35.372908 -24.348693 -27.068301 ... -1.091571 -4.029056 0.480034 -0.739063 2.568752 1.091411 6.918867 3.662456 0.324002 0.847026
137 1987 41.713453 37.335286 44.999169 44.835086 43.405317 43.689305 36.016554 -25.016356 -27.465912 ... -1.063947 -4.169568 0.335822 -0.930320 2.834639 1.379751 7.239711 4.062609 0.505025 0.986100
138 1988 42.613162 38.237733 46.061265 45.888114 43.980680 44.351753 37.159429 -25.753480 -28.212058 ... -1.315556 -3.675372 0.271529 -1.130307 2.801062 1.296993 7.186090 3.901121 0.567943 1.053166
139 1989 44.013984 39.502013 47.619583 47.407567 45.390972 45.740617 38.423154 -26.491215 -29.073343 ... -1.015355 -3.482662 0.219371 -1.194204 2.951420 1.519677 7.549320 3.981549 0.668676 1.037878
140 1990 45.142816 40.205053 48.674911 48.649309 46.436335 47.085969 39.805316 -27.450982 -30.094326 ... -0.774662 -3.110601 0.311073 -1.386043 2.974462 1.651054 7.633376 4.292774 0.235372 1.059732
141 1991 45.937118 41.121417 49.644056 49.566577 47.089135 47.478253 40.723267 -28.272119 -30.435812 ... -0.401043 -2.797485 0.659956 -1.464909 2.669275 1.376177 7.059336 4.176778 -0.248546 0.982632
142 1992 44.852366 40.520443 49.002710 48.150099 45.465259 46.213350 39.762334 -29.026027 -31.108523 ... -0.660479 -2.422381 0.825514 -1.082282 0.617449 -0.414561 5.055588 2.146524 -2.511578 -1.188725
143 1993 44.847455 40.451610 48.883294 48.420651 45.119743 45.847694 40.361738 -29.801813 -31.702964 ... -0.610450 -2.630887 0.920776 -1.266106 -0.439873 -1.535252 4.024444 1.075193 -3.704523 -2.059229
144 1994 45.989332 41.766259 49.914304 49.615651 46.269935 46.663878 41.705964 -30.452287 -32.070424 ... -0.784194 -2.946875 0.741433 -1.077784 -0.323952 -1.743465 4.161938 1.160311 -3.590069 -1.608473
145 1995 47.098108 42.886606 51.048676 50.723090 47.109604 47.765356 43.055314 -31.260093 -33.009192 ... -1.108774 -2.984650 0.952437 -0.584365 0.025227 -1.242215 4.708399 1.297451 -3.130096 -1.507402
146 1996 48.319306 44.196080 52.296175 51.819321 48.262340 48.839920 44.502002 -32.001633 -33.680326 ... -0.927239 -3.099619 0.948674 -1.060571 0.231445 -0.921329 4.923418 1.334288 -3.031554 -1.147596
147 1997 49.798270 46.226142 53.538800 53.362991 49.912142 50.160435 45.589110 -32.643873 -34.605678 ... -1.208794 -3.351336 1.092950 -1.018753 0.459012 -1.135275 4.992528 1.789482 -2.898569 -0.453104
148 1998 51.060756 47.788436 54.628491 54.307652 51.426787 51.101909 47.111262 -33.437269 -35.105833 ... -1.676849 -3.006865 0.904789 -0.804452 0.680377 -1.084158 5.435114 2.220755 -2.979758 -0.190071
149 1999 52.365222 49.013508 55.815352 55.583371 52.594615 52.370158 48.814327 -34.309374 -35.652971 ... -1.776228 -2.565577 0.745220 -1.014283 0.911828 -0.491488 5.315827 2.548642 -3.122183 0.308343
150 2000 53.664892 50.486800 56.432424 57.084052 53.996509 53.890272 50.099297 -35.098555 -36.254356 ... -1.536444 -2.653559 0.536502 -1.210960 1.058697 -0.467851 5.398064 2.879244 -2.916352 0.400378
151 2001 55.041435 52.107892 57.624444 58.390467 55.355560 55.091652 51.678598 -35.754562 -36.714402 ... -1.596728 -2.386639 0.623530 -1.112894 1.292952 0.005061 5.501922 2.937838 -2.896869 0.916809
152 2002 56.468781 53.596859 59.336683 59.846940 56.716715 56.315459 53.000029 -36.505531 -37.481802 ... -1.139592 -2.253673 0.669264 -1.265762 1.527660 0.459771 5.380845 3.255654 -2.975282 1.517310
153 2003 57.806521 54.995428 60.528331 61.356211 58.141005 57.559311 54.258839 -37.105350 -37.979499 ... -0.954355 -2.620480 0.765391 -1.076781 1.820953 0.549441 5.436436 3.697526 -2.873010 2.294373
154 2004 59.092265 56.324909 61.596290 63.051871 59.472902 58.608703 55.498918 -37.766827 -38.738321 ... -0.950624 -2.659719 0.641039 -1.466182 1.967039 0.765177 5.554431 3.861156 -2.869829 2.524262
155 2005 60.491580 57.692328 62.788338 64.576346 60.854703 60.042009 56.995755 -38.498393 -39.345031 ... -1.199984 -2.114013 0.545057 -1.496730 2.044882 1.133363 5.239558 4.075110 -2.581247 2.357626

156 rows × 44 columns

Name of different forcings.


In [17]:
forcings = ['All forcings', 'Anthropogenic tropospheric aerosol','Greenhouse gases','Land use','Ozone','Solar','Volcanic']

In [18]:
colors = ['C0','C1','C2','C3','C4','C5','C6']

fig, axes = plt.subplots(4,2, figsize=(11.5, 15))
ax=axes.flatten()
for i in np.arange(7):
    ax[i].plot(ohc['Year'],ohc[forcings[i]+' Ensemble Mean'],linewidth=3,color=colors[i])
    for k in np.arange(1,6):
        ax[i].plot(ohc['Year'],ohc[forcings[i]+' R'+str(k)],linewidth=0.5,color=colors[i])
    ax[i].set_xlabel('Year')
    ax[i].set_ylabel(r'Ocean Heat Content ($10^{22}$J)')
    ax[i].set_title(forcings[i])
    
#summary subplot
lines = []
for i in np.arange(7):
    lines+=ax[7].plot(ohc['Year'],ohc[forcings[i]+' Ensemble Mean'],linewidth=3,color=colors[i],label=forcings[i])
    for k in np.arange(1,6):
        ax[7].plot(ohc['Year'],ohc[forcings[i]+' R'+str(k)],linewidth=0.5,color=colors[i])
    ax[7].set_xlabel('Year')
    ax[7].set_ylabel(r'Ocean Heat Content ($10^{22}$J)')
    ax[7].set_title('summary')
    #ax[7].legend(handles=lines,loc="upper left")
fig.suptitle('Response of Ocean Heat Content by different forcings',y=0.93,fontsize=15)
fig.subplots_adjust(hspace=0.35)


Question 5: What is the relative contribution of each forcing to ocean heat content (positive or negative)? What's the magnitude of contributions?

  • Anthropogenic tropospheric aerosol
  • Greenhouse gases
  • Land use
  • Ozone
  • Solar
  • Volcanic

Answer 5:

Now let's read the temperature response to the same forcings. (tas = "temperature of the air at 2m height")


In [19]:
tas = pd.read_csv('tas.Marvel_etal2015.csv')

In [20]:
tas


Out[20]:
Year All forcings Ensemble Mean All forcings R1 All forcings R2 All forcings R3 All forcings R4 All forcings R5 All forcings R6 Anthropogenic tropospheric aerosol Ensemble Mean Anthropogenic tropospheric aerosol R1 ... Solar R2 Solar R3 Solar R4 Solar R5 Volcanic Ensemble Mean Volcanic R1 Volcanic R2 Volcanic R3 Volcanic R4 Volcanic R5
0 1900 0.111148 0.103798 0.162833 0.108305 0.029161 0.122152 0.140640 -0.026012 -0.020009 ... 0.010009 0.009537 -0.231299 0.042706 0.087778 0.106602 0.067836 0.085882 0.061631 0.116937
1 1901 0.148344 0.235785 0.157822 0.134350 0.082105 0.103183 0.176822 -0.047284 -0.099281 ... 0.008983 -0.065837 -0.044185 0.087776 0.053624 0.026345 0.167107 0.084815 -0.062184 0.052038
2 1902 0.108618 0.162046 0.049158 0.175059 0.018039 0.012236 0.235172 -0.024657 -0.031059 ... 0.034285 0.006739 0.010537 0.019686 0.008973 -0.027760 0.096914 -0.031996 -0.057169 0.064874
3 1903 -0.074575 -0.083274 -0.091113 -0.022231 -0.116071 -0.096675 -0.038083 -0.007300 -0.040071 ... 0.003604 -0.041466 -0.076995 0.102488 -0.169750 -0.205922 -0.219373 -0.131790 -0.149822 -0.141842
4 1904 -0.044726 -0.010342 -0.068511 -0.084272 -0.015209 -0.053399 -0.036621 -0.045635 -0.178219 ... -0.024222 -0.067896 0.006976 0.090059 -0.113867 -0.104091 -0.104014 -0.090662 -0.097495 -0.173071
5 1905 0.002761 0.023988 0.045473 -0.054548 0.044736 -0.008447 -0.034637 -0.018807 -0.101818 ... -0.020324 0.050933 -0.086893 0.131271 -0.051841 -0.081308 -0.065054 0.010467 -0.053837 -0.069475
6 1906 0.108427 0.016779 0.087650 0.105899 0.155592 0.144794 0.139849 -0.029115 -0.037684 ... -0.094657 0.053972 -0.135065 0.061406 -0.019895 0.022498 -0.086665 -0.035894 0.057003 -0.056417
7 1907 0.092086 0.097540 0.102027 -0.001093 0.159102 0.094263 0.100676 -0.049533 -0.010749 ... -0.069270 0.022075 -0.108526 0.058792 -0.034866 -0.031473 -0.040485 -0.005242 0.051355 -0.148483
8 1908 0.095825 0.119193 0.175224 -0.010094 0.136591 0.050898 0.103137 -0.054159 -0.108851 ... -0.048225 0.029610 -0.048528 0.080663 -0.030233 -0.121289 -0.049787 0.020087 -0.040745 0.040570
9 1909 0.143157 0.131294 0.166989 0.065337 0.168136 0.057891 0.269295 -0.020442 -0.031306 ... -0.010173 -0.009247 -0.002745 0.035430 0.036775 0.023160 0.013336 0.077153 0.025351 0.044873
10 1910 0.129143 0.186425 0.039764 0.212706 -0.021780 0.081058 0.276686 -0.021226 0.007444 ... -0.014450 0.086565 -0.010640 0.038777 0.018975 0.041391 -0.011398 0.069720 0.018983 -0.023819
11 1911 0.184638 0.212035 0.119327 0.177418 0.212957 0.181765 0.204327 -0.012760 0.011324 ... 0.008264 0.036338 0.018629 0.068576 0.052176 0.073211 0.079772 0.030423 0.096952 -0.019479
12 1912 0.107027 0.136343 0.151620 0.033965 0.085256 0.059408 0.175570 -0.005445 -0.015873 ... 0.028785 0.067866 -0.111321 0.049201 0.060786 0.124045 0.003779 -0.012857 0.052886 0.136079
13 1913 0.018753 0.106855 0.041792 -0.027696 -0.037871 0.022497 0.006942 -0.016115 -0.010788 ... 0.046771 0.026386 -0.110880 -0.018070 -0.031853 0.046469 -0.181821 -0.061096 -0.014384 0.051565
14 1914 0.071447 0.094009 0.172112 0.046067 0.083184 -0.076342 0.109649 -0.003353 0.090022 ... -0.001882 0.034997 -0.225369 0.118273 -0.020901 0.075417 -0.086810 -0.121479 -0.004271 0.032640
15 1915 0.114367 0.114104 0.171823 0.095790 0.102187 0.048200 0.154094 0.002143 0.015692 ... -0.002866 -0.009490 -0.063282 0.081509 0.026392 -0.062341 0.012459 0.068976 0.069610 0.043259
16 1916 0.139729 0.142537 0.168912 0.147676 0.041336 0.162434 0.175480 -0.040867 0.032096 ... 0.051997 -0.068554 -0.177578 0.033961 0.061890 0.104096 0.082384 0.109555 0.039257 -0.025843
17 1917 0.189023 0.152669 0.224610 0.119265 0.195990 0.212717 0.228889 -0.008677 0.089532 ... -0.006657 -0.012637 -0.090669 -0.015170 0.007738 0.032277 0.002638 -0.016171 0.014075 0.005873
18 1918 0.170066 0.195614 0.130797 0.120774 0.237390 0.196803 0.139021 -0.046720 0.002297 ... 0.045620 -0.062411 0.051950 -0.034791 0.029004 0.061881 0.026025 -0.010554 0.037220 0.030445
19 1919 0.192227 0.251905 0.229196 0.055357 0.293651 0.231464 0.091790 -0.042756 -0.009094 ... 0.032367 0.089988 -0.052445 0.000760 0.008917 -0.082612 -0.077475 0.010236 0.036283 0.158154
20 1920 0.184270 0.206208 0.173186 0.159475 0.238459 0.183363 0.144931 0.059258 0.010088 ... -0.050334 0.041023 -0.005725 0.025896 -0.005388 0.004952 -0.159097 0.053747 -0.021612 0.095068
21 1921 0.151623 0.188767 0.136576 0.124181 0.122025 0.156340 0.181846 -0.067728 0.063035 ... -0.039036 0.043506 -0.059754 -0.037753 0.057064 0.029621 0.010498 0.079359 0.052857 0.112984
22 1922 0.185192 0.077954 0.116999 0.134834 0.275045 0.231020 0.275301 -0.021843 -0.012933 ... -0.043902 0.043785 -0.157884 -0.006216 0.067869 0.005201 0.047093 0.059074 0.089622 0.138353
23 1923 0.232155 0.174396 0.171879 0.161316 0.276424 0.280659 0.328258 -0.031370 -0.030975 ... -0.006354 0.057028 -0.015618 0.052361 0.093892 -0.016180 0.133507 0.161285 0.039999 0.150850
24 1924 0.216975 0.182279 0.182401 0.151864 0.246234 0.295260 0.243813 -0.006563 0.016740 ... 0.058892 -0.104226 0.076082 -0.001693 0.020813 -0.087396 -0.006621 0.071140 0.103281 0.023661
25 1925 0.207999 0.277603 0.162737 0.172103 0.204797 0.173110 0.257643 -0.028266 -0.000470 ... 0.035383 0.014185 -0.081529 0.073176 0.057317 -0.022844 0.067678 0.085541 0.064994 0.091215
26 1926 0.176792 0.178050 0.115211 0.158249 0.236666 0.140190 0.232385 -0.059609 -0.050366 ... 0.041650 0.058460 0.002045 0.022543 0.047401 -0.058500 0.089634 0.052960 0.006851 0.146059
27 1927 0.224168 0.222531 0.239020 0.083993 0.268829 0.267342 0.263295 -0.045409 -0.002903 ... 0.058531 0.009693 -0.133517 -0.124229 0.036379 0.078017 0.085186 -0.033481 0.012862 0.039312
28 1928 0.223349 0.309901 0.270767 0.156728 0.170082 0.265093 0.167525 -0.064464 -0.057866 ... -0.074305 0.044799 0.019927 0.085629 0.079479 0.039007 0.142078 0.088887 0.040278 0.087147
29 1929 0.197360 0.229194 0.218751 0.131666 0.259768 0.287819 0.056961 -0.028759 -0.007344 ... 0.046764 -0.038155 -0.060251 0.030280 0.038779 -0.034482 0.091282 0.021175 -0.008073 0.123993
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
76 1976 0.415123 0.561114 0.479519 0.412359 0.351445 0.391075 0.295226 -0.306100 -0.382347 ... 0.021252 0.122014 -0.031527 -0.088628 -0.050156 -0.023158 -0.073258 -0.007324 -0.023809 -0.123231
77 1977 0.472983 0.543754 0.441238 0.448296 0.503295 0.555017 0.346299 -0.265855 -0.335331 ... 0.107059 0.113155 0.080113 -0.087557 0.035352 0.038535 0.144053 -0.019052 0.069444 -0.056224
78 1978 0.478980 0.471473 0.505453 0.406887 0.477353 0.570958 0.441755 -0.257360 -0.291669 ... 0.047971 0.030230 -0.044271 0.015968 0.014319 0.016285 0.004124 -0.077988 0.072111 0.057061
79 1979 0.512004 0.546255 0.423315 0.523296 0.471465 0.647257 0.460434 -0.345691 -0.318561 ... 0.063478 0.086147 -0.078989 0.007086 0.037434 0.114489 -0.117708 -0.025980 0.125005 0.091363
80 1980 0.537843 0.602518 0.614418 0.474510 0.476913 0.662285 0.396416 -0.363457 -0.383026 ... 0.068239 0.026982 0.049151 -0.006043 0.033067 0.132479 -0.010309 -0.111086 0.055194 0.099056
81 1981 0.595501 0.613921 0.719454 0.552013 0.517771 0.655595 0.514249 -0.401594 -0.510514 ... 0.069265 -0.023581 0.023975 0.065166 0.001412 0.017031 -0.113538 0.006037 0.072913 0.024618
82 1982 0.471870 0.474342 0.490923 0.449374 0.467684 0.480020 0.468877 -0.335799 -0.408906 ... 0.070007 -0.011851 -0.005412 0.015434 -0.066209 -0.045929 -0.069743 -0.090080 0.003032 -0.128326
83 1983 0.318538 0.281570 0.353182 0.263055 0.293202 0.438885 0.281334 -0.338329 -0.445611 ... 0.042867 0.077809 -0.014022 0.114037 -0.274654 -0.208005 -0.296069 -0.286034 -0.258507 -0.324658
84 1984 0.410942 0.408851 0.356678 0.419643 0.460705 0.506395 0.313382 -0.399697 -0.552602 ... 0.054622 0.122135 0.047996 0.101350 -0.205219 -0.229286 -0.294910 -0.248377 -0.050217 -0.203306
85 1985 0.484051 0.461258 0.492314 0.478283 0.492572 0.606323 0.373554 -0.360806 -0.460464 ... 0.016468 0.075044 0.095918 -0.015191 -0.099998 -0.066494 -0.096282 -0.168357 -0.042061 -0.126795
86 1986 0.505956 0.462641 0.444134 0.525939 0.555038 0.587096 0.460885 -0.387838 -0.414311 ... 0.058357 0.001625 -0.001078 0.026126 -0.038172 -0.003914 -0.041243 -0.087026 -0.013319 -0.045359
87 1987 0.573546 0.505635 0.410780 0.675965 0.744918 0.576842 0.527138 -0.393344 -0.481827 ... 0.047462 0.046313 0.070226 0.025448 -0.028612 -0.116283 0.021152 0.011082 -0.073722 0.014709
88 1988 0.597030 0.593092 0.543432 0.587328 0.675244 0.740511 0.442574 -0.405126 -0.476704 ... 0.093810 -0.054712 0.107589 0.064768 -0.007917 -0.083068 0.054112 -0.016208 0.053588 -0.048011
89 1989 0.625084 0.592989 0.547351 0.587134 0.677538 0.749424 0.596070 -0.401777 -0.481597 ... 0.025299 0.020245 0.042725 0.013040 0.009418 -0.037533 -0.063801 -0.051037 0.116750 0.082710
90 1990 0.680359 0.654356 0.689825 0.710017 0.779691 0.703968 0.544297 -0.361909 -0.503946 ... 0.029407 0.047592 -0.030721 0.015769 0.060729 -0.052538 0.168948 -0.111891 0.184244 0.114884
91 1991 0.634999 0.497680 0.634377 0.629078 0.769926 0.753164 0.525770 -0.397324 -0.503717 ... -0.023548 0.023833 -0.111309 -0.026838 -0.037210 -0.110133 0.083609 -0.104779 0.038755 -0.093500
92 1992 0.349664 0.249443 0.321596 0.408767 0.528427 0.319078 0.270674 -0.380013 -0.439319 ... 0.113921 0.024156 0.024993 -0.037851 -0.349630 -0.438235 -0.278561 -0.356468 -0.290831 -0.384054
93 1993 0.420967 0.383666 0.474844 0.405094 0.482173 0.572944 0.207079 -0.420935 -0.551130 ... 0.060360 -0.028738 -0.042315 0.059879 -0.277545 -0.345528 -0.221014 -0.261012 -0.177757 -0.382413
94 1994 0.515932 0.422266 0.571298 0.511355 0.510200 0.582382 0.498094 -0.436798 -0.598604 ... 0.074430 -0.010955 0.011446 0.009410 -0.165309 -0.139522 -0.030760 -0.160774 -0.187333 -0.308157
95 1995 0.643489 0.600785 0.687511 0.674946 0.664989 0.636889 0.595813 -0.395940 -0.401934 ... 0.000934 -0.068793 0.079864 0.008645 -0.049528 -0.080968 -0.082578 0.023875 0.011938 -0.119908
96 1996 0.662736 0.581170 0.734708 0.713921 0.621494 0.687592 0.637528 -0.427642 -0.423953 ... 0.025317 0.025877 0.111434 0.143972 0.029677 -0.018374 0.069699 0.108041 0.083670 -0.094652
97 1997 0.695971 0.594463 0.790369 0.707251 0.615929 0.715692 0.752121 -0.417494 -0.452678 ... 0.087596 0.007067 -0.047175 0.047738 0.051446 0.066404 0.108994 0.031011 0.081738 -0.030919
98 1998 0.789057 0.764938 0.786144 0.837616 0.787816 0.803103 0.754726 -0.427181 -0.512325 ... 0.034384 0.023805 0.068252 0.009901 0.086145 0.117291 0.083796 0.096286 0.152547 -0.019195
99 1999 0.826917 0.807288 0.872431 0.799407 0.856815 0.799004 0.826558 -0.403596 -0.399145 ... 0.054879 0.080427 0.078425 0.152417 0.081310 0.141172 0.156668 0.089428 0.072943 -0.053661
100 2000 0.867067 0.781659 0.940467 0.824878 0.845077 0.927130 0.883193 -0.418617 -0.429962 ... 0.077497 0.017554 0.077140 0.128262 0.055718 0.159101 0.068958 0.073541 -0.026642 0.003631
101 2001 0.879018 0.906133 0.913399 0.864276 0.856020 0.844663 0.889617 -0.473157 -0.479815 ... 0.126581 -0.035505 0.018080 0.071667 0.057264 0.021926 0.052448 0.066193 0.108285 0.037470
102 2002 0.893343 0.853782 0.913512 0.854393 0.967677 0.830992 0.939702 -0.419215 -0.436809 ... 0.043390 0.050477 0.071645 0.109107 0.062523 0.091913 0.060656 0.063709 0.100803 -0.004467
103 2003 0.906375 0.898436 1.011491 0.865386 0.868816 0.899509 0.894609 -0.408363 -0.437024 ... 0.109404 0.001148 0.042307 0.045307 0.073310 0.193690 0.026583 0.098923 0.135287 -0.087934
104 2004 0.940968 0.949198 0.891976 0.931764 0.969831 1.021655 0.881386 -0.421039 -0.506679 ... 0.129596 -0.101249 0.075709 0.110206 0.092095 0.113348 0.084934 0.081376 0.129697 0.051119
105 2005 0.921846 0.912318 0.887900 0.995325 0.886189 0.985361 0.863982 -0.441584 -0.478042 ... 0.052786 -0.085191 0.056573 0.034504 0.095834 0.115567 0.100917 0.077982 0.030537 0.154165

106 rows × 44 columns


In [21]:
fig, axes = plt.subplots(4,2, figsize=(11.5, 15))
ax=axes.flatten()
for i in np.arange(7):
    ax[i].plot(tas['Year'],tas[forcings[i]+' Ensemble Mean'],linewidth=3,color=colors[i])
    for k in np.arange(1,6):
        ax[i].plot(tas['Year'],tas[forcings[i]+' R'+str(k)],linewidth=0.5,color=colors[i])
    ax[i].set_xlabel('Year')
    ax[i].set_ylabel('Temperature anomalies (K)')
    ax[i].set_title(forcings[i])
    
#summary subplot
lines = []
for i in np.arange(7):
    lines+=ax[7].plot(tas['Year'],tas[forcings[i]+' Ensemble Mean'],linewidth=3,color=colors[i],label=forcings[i])
    for k in np.arange(1,6):
        ax[7].plot(tas['Year'],tas[forcings[i]+' R'+str(k)],linewidth=0.5,color=colors[i])
    ax[7].set_xlabel('Year')
    ax[7].set_ylabel('Temperature anomalies (K)')
    ax[7].set_title('summary')
    #ax[7].legend(handles=lines,loc="upper left")
fig.suptitle('Response of Temperature to different forcings',y=0.93,fontsize=15,weight='bold')
fig.subplots_adjust(hspace=0.35)


Question 6: What is the relative contribution of each forcing to surface temperature (positive or negative)? What is the magnitude of contributions? Do this for:

  • Anthropogenic tropospheric aerosols
  • Greenhouse gaseses
  • Land use
  • Ozone
  • Solar irradiance
  • Volcanic aerosols

Answer 6:

Question 7: Compare the summary plot of surface temperature to ocean heat content? Which one has more variability (wiggles)? Based on what you learned from the lectures, why is the ocean heat content a better indicator of climate change compared with surface temperature?

Answer 7:


In [ ]: