CORDEX in the IPCC WGI perspective
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1 CORDEX in the IPCC WGI perspective Are regional patterns of chance robust? Jens H. Christensen, Fredrik Boberg, Philippe Lucas-Picher* and Ole B. Christensen Kanikicharla Krishna Kumar** Danish Meteorological Institute *now Meteo France ** Indian Institute of Tropical Meteorology
2 Outline Room for CORDEX? One IPCC WG-I view Aspects of RCM model bias Model bias as the modelers challenge Learning from organized ensembles modeling Exploring the consequence of systematic errors Increasing confidence in projections
3
4 AR5 WG-I outline Chapter 1: Introduction Chapter 2: Observations: Atmosphere and Surface Chapter 3: Observations: Ocean Chapter 4: Observations: Cryosphere Chapter 5: Information from Paleoclimate Archives Chapter 6: Carbon and Other Biogeochemical Cycles Chapter 7: Clouds and Aerosols Chapter 8: Anthropogenic and Natural Radiative Forcing
5 AR5 WG-I outline Chapter 9: Evaluation of Climate Models Chapter 10: Detection and Attribution of Climate Change: from Global to Regional Chapter 11: Near-term Climate Change: Projections and Predictability Chapter 12: Long-term Climate Change: Projections, Commitments and Irreversibility Chapter 13: Sea Level Change Chapter 14: Climate Phenomena and their Relevance for Future Regional Climate Change Annex I: Atlas of Global and Regional Climate Projections
6 9. Evaluation of Climate Models The hierarchy of climate models: from global to regional Downscaling methods Assessing model performance, including quantitative measures and their use New model components and couplings Representation of processes and feedbacks in climate models Simulation of recent and longer term records Simulation of regional patterns, variability and extremes
7 Chapter 9 Coordinating Lead Authors Gregory FLATO Jochem MAROTZKE Lead Authors Babatunde ABIODUN Pascale BRACONNOT Sin Chan CHOU William COLLINS Peter COX Fatima DRIOUECH Seita EMORI Veronika EYRING Chris FOREST Peter GLECKLER Eric GUILYARDI Christian JAKOB Vladimir KATTSOV Chris REASON Markku RUMMUKAINEN CANADA GERMANY SOUTH AFRICA FRANCE BRAZIL USA UNITED KINGDOM MOROCCO JAPAN GERMANY USA USA FRANCE AUSTRALIA RUSSIA SOUTH AFRICA SWEDEN
8 12. Long-term Climate Change: Projections, Commitments and Irreversibility Scenario description Projections for the 21st century RCP experiments Projections beyond the 21st century Regional climate change, variability and extremes Forcing, response and climate sensitivity Climate change commitment and inertia Potential for abrupt change and irreversibility in the climate system Quantification of the range of climate change projections
9 Chapter 12 Coordinating Lead Authors Matthew COLLINS Reto KNUTTI Lead Authors Julie ARBLASTER Ken CALDEIRA Jean-Louis DUFRESNE Thierry FICHEFET Pierre FRIEDLINGSTEIN Xuejie GAO William GUTOWSKI Tim JOHNS Gerhard KRINNER Mxolisi SHONGWE Claudia TEBALDI Andrew WEAVER Michael WEHNER UNITED KINGDOM SWITZERLAND AUSTRALIA USA FRANCE BELGIUM UNITED KINGDOM CHINA USA UNITED KINGDOM FRANCE SWAZILAND USA CANADA USA
10 Chapter 14 Changes in Local Ambient Conditions Climate modes of variability Tropical modes Northern Hemisphere climate modes Southern Hemisphere modes
11 Chapter 14 Regional Changes Asia Indian Monsoon East-Asian Monsoon Indo-Australian Monsoon including Maritime Continent Western and North Pacific Monsoon Northern Asia Australia and New Zealand Africa Europe and Mediterranean Americas Polar Small Islands
12 Chapter 14: Climate Phenomena and their Relevance for Future Regional Climate Change Length: 30 printed pages excl. refs and figures and FAQ CLAs: J.H. Christensen and K. Krishna Kumar LAs:E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, V. Dong, P. Goswami, A. Hall, J. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwich, D. Stephenson, S.-P. Xie, T. Zhou
13 Annex I Annex I: Atlas of Global and Regional Climate Projections will provide comprehensive information on a selected range of variables (e.g., temperature and precipitation) for a few selected time horizons (e.g., 2020, 2050, and 2100) for all regions and, to the extent possible, for the four basic RCP scenarios. Numerical fields corresponding to the figures together with the specification of the data sources and the description of how the figures were constructed will also be included.
14 Annex I The information used in Annex I will be based on material assessed in WGI Chapters 11, 12 or 14. Each Figure caption will include a reference to the location in the assessment report where the underlying information is assessed and vice versa. Each figure included in the Atlas will be assessed and reviewed as part of the underlying chapter in which it originates.
15 Annex I Editorial Team: Mat Collins (Ch12 CLA, Chair of Editorial Team), Julie Arblaster (Ch12 LA), Jens Hesselbjerg Christensen (Ch14 CLA), Geert Jan van Oldenborgh (Ch11 LA), Scott Power (Ch11 LA), Tianjun Zhou (Ch14 LA) Advisory Board: David Wratt (WGI ViceChair), Frances Zwiers (WGI Vice-Chair), Bruce Hewitson (WGII CLA)
16 A1B scenario Curtsey van Oldenborgh (AR5, Atlas)
17 ENSEMBLES Summer temperature model bias Weighted Unweighted Bias OBS Christensen et al. 2010
18 Relating model spread and uncertainty assignment Delta change and transient change assumes invariance of model bias under climate change What if this is not the case? What are the implications for the predictions? Can this possibly be ameliorated?
19 <Tchange>= <T T > DJF JJA
20 Models and regions + gridded E-OBS dataset GCM runs from CMIP3
21 Model bias vs. observations 25% warmest (Christensen et al. 2008)
22 Model bias vs. model values
23 Assessing climate change
24 Assessing climate change
25 Assessing climate change
26 Assessing climate change 40% warmest
27 Assessing climate change?????
28 Tbias for ERA40 Scandinavia (cold climate) Mediterranean (warm and dry) Eastern Europe (mixed climate)
29 <Tchange>= <T T > DJF JJA
30 Ranked Tmod for RCMs/GCMs (Eastern Europe) 11 RCMs 64 GCMs
31 <Tchange>= <T T > DJF JJA
32 <Tchange>= <T T > DJF JJA
33 <Tchange>= <T T > - <BC> Bias Corr. No bias Corr. DJF JJA
34 Mediterranean
35 A1B scenario Curtsey van Oldenborgh (AR5, Atlas)
36 Conclusions RCMs are suffering from non-linear biases in casu temperature These impact climate change results, particular when entering non-experienced regimes Applying a bias correction reduce what seems to be well established geographical warming patterns. Model spread is reduced Implications for extremes are not easy to assess, but could be substantial The same physical mechanism appears to be at play in GCMs!
37 Thank you for your attention
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