Earth System Modelling and Evaluation with Observations

Transcription

1 Earth System Modelling and Evaluation with Observations Veronika Eyring Deutsches Zentrum für Luft- und Raumfahrt e.v. (DLR) Institute of Atmospheric Physics, Oberpfaffenhofen, Germany 9 November 2015 JPI CLIMATE WORKSHOP ON EUROPEAN LONG TERM OBSERVATION NETWORKS 9-10 November 2015, PARIS

2 CMIP: Understanding past, present and future climate Since 1995, CMIP has coordinated climate model experiments involving multiple international modeling teams worldwide. CMIP has led to a better understanding of past, present and future climate change and variability. CMIP model simulations have also been regularly assessed as part of the IPCC Climate Assessments Reports and various national assessments. CMIP is a project of the World Climate Research Programme (WCRP) s Working Group of Coupled Modelling (WGCM) CMIP s central goal is to advance scientific understanding of the Earth system. CMIP has developed in phases, with the simulations of the fifth phase, CMIP5, now mostly completed. Though analyses of the CMIP5 data will continue for at least several more years, science gaps and outstanding science questions have prompted preparations to get underway early for the sixth phase of the project (CMIP6).

3 CMIP6 Organization CMIP Panel (V. Eyring (chair), J. Meehl, B. Stevens, R. Stouffer, K. Taylor) which is responsible for direct coordination of CMIP and overseeing the whole CMIP process. Sub-committee of WCRP s Working Group of Coupled Modelling (WGCM, co-chairs S. Bony and C. Senior). WGCM Infrastructure Panel (WIP, co-chairs V. Balaji & K. Taylor): Establishes standards and policies for sharing climate model output; puts the data request together technically (M. Juckes). CMIP6 Design Based on an extensive period (two years) of community consultation and workshops Initial proposal for the design of CMIP6 (Meehl et al., EOS, 2014). Feedback on this initial CMIP6 proposal has being solicited over the year from modeling groups and model analysts until September The WGCM and the CMIP Panel have then finalized the CMIP6 design at the WGCM 18th session (October 2014, Grainau) in consultation with the model groups and MIP co-chairs.

4 CMIP6 Design: Scientific Focus The scientific backdrop for CMIP6 is the WCRP Grand Challenges: 1. Clouds, Circulation and Climate Sensitivity 2. Changes in Cryosphere 3. Climate Extremes 4. Regional Sea-level Rise 5. Water Availability 6. Decadal Predictability (pending) 7. Biogeochemical forcings and feedbacks (pending) The specific experimental design is focused on three broad scientific questions: 1. How does the Earth System respond to forcing? 2. What are the origins and consequences of systematic model biases? 3. How can we assess future climate changes given climate variability, predictability and uncertainties in scenarios?

5 DECK Experiments & CMIP6 Historical Simulation Ongoing Diagnosis, Evaluation, and Characterization of Klima (DECK) Experiments DECK (entry card for CMIP) i. AMIP simulation (~ ) ii. Pre-industrial control simulation iii. 1%/yr CO 2 increase iv. Abrupt 4xCO 2 run CMIP6 Historical Simulation (entry card for CMIP6) v. Historical simulation using CMIP6 forcings ( ) Note: The themes in the outer circle of the figure might be slightly revised at the end of the MIP endorsement process (DECK & CMIP6 Historical Simulation to be run for each model configuration used in the subsequent CMIP6-Endorsed MIPs)

6 CMIP6-Endorsed Model Intercomparison Projects (MIPs)

7 Models are increasing in complexity and resolution - From AOGCMs to Earth System Models with biogeochemical cycles km resolution orography Atmospheric Chemistry 25 km resolution orography

8 Expanded scope to address increasingly compelling and interdisciplinary science questions Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board Building bridges between the Modeling and Applications communities - A historic development in our field that provides, for the first time in CMIP, an avenue for a more formal communication with the VIACS community - Sectors: Agriculture, Forestry, Energy Water Resources and Hydrology Oceans/Fisheries, Coastal, Urban, Health Infrastructure/Transportation, etc. Projects and Programs: TGICA, CORDEX, ICONICS WCRP WGRC ISI-MIP, AgMIP, WaterMIP Facilitates two-way communication around science and application goals: Informed use of model outputs Design of online diagnostics, metrics, and visualizations of relevance to society. Feedback from VIACS community so far has included several strong messages: Most VIACS applications are grounded in CMIP6 Historical & ScenarioMIP simulations. Large diversity in requests for variables (beyond temperature and rainfall) and experiment priorities from different sectors).

9 Routine Benchmarking and Evaluation Central Part of CMIP6 CMIP evaluation tool to produce well-established analyses as soon as model output becomes available e.g., Community-developed ESM Evaluation Tool (Eyring et al., GMDD, 2015) and PCMDI metrics package - Link to WGNE/WGCM Climate Model Metrics Panel Similar to Figure 9.7 of AR5 Monsoon Precipitation Intensity and Domain CMIP5 MMM CMIP5 MMM - OBS Running along-side the ESGF AR5 Chapter 9 Similar to Figure 9.7 of AR5 Link to projections Similar to Figure 9.5 of AR5 Similar to Figure 9.24 of AR5 Similar to Figure 9.24 of AR5

10 Under-Exploited Observations for Model Evaluation Observations for Model Intercomparison Projects (obs4mips) WDAC Task Team on Observations for Model Evaluation CMIP6 How to bring as much observational scrutiny as possible to the CMIP/IPCC process? How to best utilize the wealth of satellite observations for the CMIP/IPCC process? Obs4MIPs has defined a set of technical specifications and criteria for developing observational data sets that are technically aligned with CMIP model output (with common file format, data and metadata structure). Over 50 datasets that conform to these standards are now archived on the ESGF alongside CMIP model output (Teixeira et al., 2014), including ESA CCI data Data users have enthusiastically received Obs4MIPs

11 Examples: Ground-based Networks for Aerosol Evaluation AERONET: global, AOD and other optical properties (since early 90s) CASTNET, IMPROVE: North America, aerosol concentrations (since late 80s) EMEP: Europe, aerosol concentrations (since early 70s) EANET: Asia, aerosol concentrations (since 2000) ADVANTAGES High spatial coverage in the vicinity of strong emission sources Relatively long temporal coverage (several decades for the well established networks) High quality of the data (AERONET used as reference for satellite validation) DISADVANTAGES Limited coverage over the oceans (only a few marine stations) Low temporal resolution, due to the use of filters (CASTNET weekly, IMPROVE 3- days, EMEP mostly daily/weekly)

12 Example Satellite Data: ESA-CCI Aerosol Dataset o First aerosol data from ESA-CCI now available o Long term climate data record based on 2 data sets ERS2-ATSR2 ( ) ENVISAT-AATSR ( ) o Satellite data processed with different algorithms: University of Swansea is the recommended one (after validation with ground-based observations) o Data provided as Level 3 daily data. o The data includes the following variables: AOD at 550, 555, 659, 865, 1610 nm (mean, uncertainty and std. dev.) Angstrom exponent 555/659 and 555/865 (mean, uncertainty and std. dev.) Fine mode AOD, absorbing AOD, dust AOD (mean and std. dev.) Surface reflectance at 555, 659, 865, 1610 nm (mean and std. dev.) Cloud fraction (mean and std. dev.) Land area fraction (mean and std. dev.) Solar and satellite zenith angle (mean and std. dev.) Relative azimuth angle (mean and std. dev.)

13 Evaluation of CMIP5 models with ESA CCI Data As in Fig of IPCC Chapter 9. AOD integrated over the oceans for CMIP5 models, MODIS and ESA-CCI

14 Considering Observational Uncertainty Observational uncertainty needs to be considered in the assessment of model performance and should be an integral part of the evaluation tools. The ESMValTool is quite flexible and can consider observational uncertainty in many ways, e.g.: Uses more than one observational dataset to directly evaluate the models Show the difference between the reference dataset and the alternative observations in addition to the difference between the model and the reference datasets Could also easily use an observed uncertainty ensemble Spans the observed uncertainty space, consistent with statistics For example, multiple realizations now available for HadCRUT4 surface temperature, RSS MSU/AMSU Atmospheric Temperature (Talk Carl Mears, Obs4MIPs-CMIP6 meeting, Washington, April 2014) 5 (out of 100) realizations of HADCRUT4 However, often the problem is that the uncertainty in the observations is not readily available. Reliable and defendable error characterization/estimation of observations is a high priority throughout the community, and obs4mips should press harder for the inclusion of these estimates as part of each dataset

15 Future Emphasis and Needs (1) Requirements for the long-term observing network ESM evaluation requires the availability of consistent, error-characterized global and regional Earth observations as well as accurate reanalyses. Fully exploit available observations for ESM evaluation considering uncertainties. Improve comparability between models and observations (e.g., satellite simulators). The quality and quantity of available data, and their main gaps In many cases, the lack or insufficient quality of long-term observations, be it a specific variable, an important processes, or a particular region (e.g., polar areas, the upper troposphere/lower stratosphere (UTLS), and the deep ocean), remains an impediment (Chapter 9, IPCC AR5). More high resolution data than in CMIP5 are required Increasing complexity of ESMs requires additional observations across the atmosphere, ocean and terrestrial domains (e.g., permafrost, ice sheets). High temporal resolution (sub-daily) datasets that can be used to assess changes in extremes, including especially those with high impacts on humans and the environment, such as drought, heat waves, floods, and storms. An in-depth analysis of the underlying controls of the seasonal cycle, interannual variability and long-term temporal trends and processes can help to understand the spread in model projections over the coming decades => Process-oriented ESM evaluation requires observations beyond the currently defined ECVs.

16 Future Emphasis and Needs (2) Opportunities for a more efficient use of observing data in ESM evaluation towards the establishment of seamless connections with the ESM community. Facilitating sharing of resources Identify additional observations to include in obs4mips with guidance from WCRP WDAC Task Team on Observations for Model Evaluation. Develop and share common diagnostic tools such as the Earth System Model Evaluation (ESMValTool) that will be routinely run on CMIP6 output and according observations (obs4mips) and reanalyses (ana4mips) alongside the Earth System Grid Federation (ESGF) Encouraging the development and use of sophisticated diagnostics and methodologies of model-data comparisons so as to facilitate the characterization, the quantification and the physical understanding of model deficiencies.