Validation of Climate Models Simon Tett 19/6/06 with thanks to: Keith Williams, Mark Webb, Mark Rodwell, Roy Kershaw, Sean Milton, Gill Martin, Tim Johns, Jonathan Gregory, Peter Thorne, Philip Brohan & © Crown copyright John Caesar Page 1 Approaches Parameterisation & Forecast error Climatology's Climate Variability Climate Change. © Crown copyright Page 2 Methodology for improving parametrizations CRMs Observations Forcing Evaluation Evaluation g Fo Analysis Development in rc rc on in Fo ati g alu Ev NWP Forecast SCMs Development Forcing Evaluation (consistent errors?) © Crown copyright Page 3 Tropical Systematic Biases in NWP Moisture balance from Thermodynamic (RH) Profile Errors Idealised Aquaplanet – suggest Vs ARM Manus Sondes JAS 2003– convection largest contributor to Errors in convection? humidity biases Convection too shallow? 20 hPa Unrealistic 77 Moistening and drying 192 in forecasts 367 576 777 Moister at cloud base & Freezing level 922 © Crown copyright Page 4 S.Milton & M.Willett NWP Tropical T, RH, and Wind Errors vs Sondes Summer 2005 – Impact of new Physics A package of physics improvements introduced in March 2006 Improved Thermodynamic Profiles • Adaptive detrainment (conv) –> improve detrainment of moisture. • Changes to marine BL –> reduce LH fluxes Old • Non-gradient momentum stress. -> Improve low level winds Model New Physics ΔRH Day 5 Circulation Errors In Equatorial Winds Reduced ΔT Day 5 S. Derbyshire, A. Maidens, A. Brown, J.Edwards, M.Willett & S.Milton © Crown copyright Page 5 Spinup Tendencies Used to assess the contribution of individual physical parametrisations and dynamics to systematic errors. Run a ~60-member ensemble of 1 to 5 day integrations started from operational NWP analyses scattered evenly over a period e.g. December to February. Useful only when total spin-up tendency resembles model bias in full simulation. Total tendency shows warming 30 to 60 N/S between 800 and 250 hPa, similar to change in full model. Upper level warming attributed to dynamics. Mid-tropospheric warming attributed to changes in cloud and precipitation: intensified hydrological cycle © Crown copyright Page 6 Climatology © Crown copyright Page 7 Storm tracks Climatology N144 Plots show differences in the standard deviation of band- pass filtered 500 hPa height in DJF, between different resolution runs and ERA, for the northern and southern hemispheres. As resolution increases, the N96 storm tracks strengthen and move polewards. Agreement with ECMWF reanalyses improves with resolution. N48 © Crown copyright Page 8 Dynamics – Zonal mean zonal winds (DJF) HadGEM1 HadGEM1 – HadCM3 HadCM3 – ERA HadGEM1 – ERA © Crown copyright Page 9 SST and SSS errors after ~300 years: HadGEM1 and HadCM3 © Crown copyright Page 10 Simulation of HIRS water vapour channel HIRS-12 channel used extensively in studies of upper tropospheric water vapour Shows 20-year mean fields for JJA Model forced with observed SSTs Main features well reproduced – model has a tendency to be too dry in sub-tropics, corresponding to an over vigorous Hadley circulation Key: Like-with-like comparison © Crown copyright Page 11 Climate Prediction Index skill scores: HadGEM1 vs. HadCM3 ISCCP clouds © Crown copyright Page 12 Validating Regional Climate Models RCM consistency realism Observations “Quasi-observational” BCs (“re-analysis”) allow us an alternative validation of the RCM BCs are from an atmosphere-only GCM which was constrained to observations from satellites, sondes, land stations, ships, buoys, etc. The RCM is forced by representations of reality both externally (e.g. observed SST) and internally (quasi-observed BCs) Thus allowing the possibility of RCM vs. observations © Crown copyrightcomparisons for particular time periods or events Page 13 Summer Wet day frequency over the Alps O B S © Crown copyright Page 14 Good Climatology not very strong constraint on future change CCSM3 CGCM3.1(T47) 3 CGCM3.1(T63) CNRM-CM3 CSIRO-Mk3.0 ECHAM/MPI-OM 2 ECHO-G FGOALS-g1.0 K GFDL-CM2.0 GFDL-CM2.1 1 GISS-AOM GISS-EH GISS-ER INM-CM3.0 0 IPSL-CM4 MIROC3.2(hires) MIROC3.2(medres) 2000 2020 2040 2060 2080 2100 MRI-CGCM2.3.2 PCM UKMO-HadCM3 UKMO-HadGEM1 © Crown copyright Page 15 Climate Variability © Crown copyright Page 16 HadGEM1 tropical behaviour Interannual standard dev of seasonal mean SSTs ▬ HadISST/ ▬ HadCM3 ▬H © Crown copyright Page 17 HadGEM1 wintertime sea ice variability related to NAO March Ice Concentration Anomalies HadGEM1 HadISST (from ERA40 PMSL) © Crown copyright Page 18 Climate Change Direct test. But what matters? © Crown copyright Page 19 Free-atmosphere temperatures © Crown copyright Page 20 Recent warming can be simulated when man-made factors are included © Crown copyright Page 21 Observed temperature change over North America, Asia and Europe and model simulation with natural and man-made factors Hadley Centre C ° 1.0 North America Asia Europe 0.5 0 Temperature change -0.5 1900 2000 1900 2000 observations Natural factors Natural + man Forcing is important! © Crown copyright Page 22 Observed & simulated climate extremes Warm nights (above 90th percentile of OBSObs Warm nights trend (Tn90p) minimum temperature), 1951-2003 90N 45N 0 45S 90S 180 90W 0 90E Days −6−4−20246 HadCM3HadCM3 Warm nights (Tn90p) HadGEM1HadGEM1 Warm nights (Tn90p) 90N 90N 45N 45N 0 0 45S 45S 90S 90S 180 90W 0 90E 180 90W 0 90E Days Days −6−4−20246 −6−4−20246 © Crown copyright Page 23 Ocean Heat Content © Crown copyright Page 24 Human-induced warming of the ocean has been detected Whereas natural internal variability (blue range) is not consistent with the observed signal (red circles), simulated ocean warming due to anthropogenic factors (green range) is consistent with the observed changes and reproduces many of the different responses seen in the individual ocean basins. Barnett et al., Science (2005) © Crown copyright Page 25 Sea Level © Crown copyright Page 26 Using satellite data ISCCP Need ISCCP simulator – clouds are seen from space. Direct simulation of radiances. © Crown copyright Page 27 ISCCP observational cloud regimes (20N-20S) © Crown copyright Page 28 Simulation of SEVIRI shortwave channels 0.6 microns 0.8 microns 1.6 microns Observed Model © Crown copyright Page 29 Observational Uncertainty Climate Records are corrected. Correction is uncertain. In situ data are point measurements and have error. Need to develop methods to use these uncertainties which have complex structure. © Crown copyright Page 30 Global time-series at smoothed annual resolution 0.6 0.4 0.2 Stn, Sampling, coverage & Stn, Sampling & Bias coverage 0 Anomaly (C) -0.2 -0.4 Stn and Sampling -0.6 -0.8 1850 1875 1900 1925 1950 1975 2000 Year (AD) © Crown copyright Page 31 © Crown copyright Page 32 How could climate-quality reanalysis help. Allow broader range of validation studies Particularly for low-frequency variability and change. Issues: Model Bias Data homogenisation. Needs error estimates. Or at least subjective views on reliability & Uncertainty. Red/Amber/Green lights ??? Provide initial conditions for “Transpose AMIP” And Boundary Conditions for regional models Would like to test their ability to downscale climate change. © Crown copyright Page 33 Do we have it now? Time mean Temperature T From 1/ 1/1958 to 1/ 1/1979 −0.3 −0.2 0 0.1 0.3 0.50.751 1.5 2 −0.75 −0.4 −0.3 −0.4−0.3 −0.3 −0.5 −0.5 1 0.75 58-79 −0.1 −0.2 −0.1 200 0.4 0 1.5 −0.1 0.3 −0.5−0.4 400 0.1 0 Pressure (hPa) −0.2 600 −0.3 −0.1 −0.3 −0.3 −0.2 −0.4 0.2 0.4 0.5 0.3 0.1 0.2 0.4 800 −0.2 0 90oN 45oN 0 45oS 90oS Latitude −2 −0.75 −0.3 0 0.3 0.75 2 Time mean Temperature T From 1/ 1/1979 to 1/ 1/2002 −0.5 −1 −0.5−0.3−0.4 −1 −0.5−0.3−0.2−0.1−0.75−0.40 −0.4 −0.75 −0.5 −0.75 −0.4 −0.10.1−0.3−0.4 0 −0.20.1−0.1−0.3 0−0.5−0.3 0.20.3 −1.5 200 0.4 0.5 −0.4 −1 −0.1−0.2 −0.5−0.2 0.30.1 0.2 0.2 0.20.30 0.4 0.1 400 0.5 0 −0.2 −0.1 Pressure (hPa) 0.5 600 0.4 0.4 0.2 0.1 79-02 800 0.2 0.3 90oN 45oN 0 45oS 90oS Latitude © Crown copyright −2 −0.75 −0.3 0 0.3 0.75 2 Page 34.