Land-Atmosphere Feedback in the Sahel Randal Koster Global Modeling and Assimilation Office NASA/GSFC Greenbelt, MD randal.d.koster@nasa.gov Organization of Talk 1. Overview of the processes that control land-atmosphere feedback. (Case study: North America) 2. Application of these ideas to the Sahel: do the observations support the existence of feedback there? 3. Model study of the controls on Sahelian rainfall variability. Warm season precipitation variance is often high in transition zones between dry and wet areas. Example: North America July Rainfall: Mean [mm/day] July Rainfall: Variance [mm2/day2] Observations (Higgins, 50-yr dataset) 8.0 5.0 3.2 2.0 1.3 0.8 0.5 0.32 0.20 0.13 0. Koster et al., GRL, 40, 3004 More evidence: tree ring data! (360 years of proxy precipitation data put together by H. Fritts, U. Arizona) Jul/Aug precipitation variances at each tree ring site Shading: Mean annual precipitation (GPCP) White dots: Locations of tree ring sites with Jul/Aug precipitation variances in top half of range Q: Do we have any reason to suspect that precipitation variances should be amplified in transition zones? A: Yes. Transition zones are more amenable to landatmosphere feedback. Precipitation wets the surface... …causing soil moisture to increase... …which causes evaporation to increase during subsequent days and weeks... …which affects the overlying atmosphere (the boundary layer structure, humidity, etc.)... …thereby (maybe) inducing additional precipitation Observed s2P s2 Feedback enhances P through the enhancement of P autocorrelation (on timescales of days to weeks). Pn correlates with Pn+2 means that Pn Pn+2 correlates with En+2 wn wn+2 correlates with correlates with correlates with Observed s2P s2 Feedback enhances P through the enhancement of P autocorrelation (on timescales of days to weeks). Pn correlates with Pn+2 means that Pn correlates with Pn+2 Breaks down in western US: low soil moisture memory wn En+2 wn+2 correlates with Breaks down in western US: correlates low evaporation with correlates with Observed s2P s2 Feedback enhances P through the enhancement of P autocorrelation (on timescales of days to weeks). Pn correlates with Pn+2 means that Pn Pn+2 correlates with En+2 wn wn+2 correlates with correlates with correlates with Breaks down in eastern US: low sensitivity of evaporation to soil moisture Observed s2P s2 Feedback enhances P through the enhancement of P autocorrelation (on timescales of days to weeks). Pn correlates with Pn+2 means that Pn Only in the center of the country (in the wet/dry transition zone) are all conditions ripe for feedback Pn+2 correlates with En+2 wn wn+2 correlates with correlates with correlates with We therefore have reason to believe that landatmosphere feedback can help explain the patterns of observed precipitation variances. Note: up to this slide, we haven’t looked at any model results! What can AGCMs tell us? July Rainfall: Mean [mm/day] July Rainfall: Variance [mm2/day2] Correlations (pentads, twice removed) [dimensionless] AGCM AGCM, no feedback Observations (Higgins, 50-yr dataset) 0.50 0.24 0.16 0.12 0.08 0. -0.08 -0.12 -0.16 -0.50 -0.24 8.0 5.0 3.2 2.0 1.3 0.8 0.5 0.32 0.20 0.13 0. same plots as before July Rainfall: Mean [mm/day] July Rainfall: Variance [mm2/day2] Correlations (pentads, twice removed) [dimensionless] AGCM bulls-eye in model is definitely induced by feedback! AGCM, no feedback Observations (Higgins, 50-yr dataset) 0.50 0.24 0.16 0.12 0.08 0. -0.08 -0.12 -0.16 -0.50 -0.24 8.0 5.0 3.2 2.0 1.3 0.8 0.5 0.32 0.20 0.13 0. The observations show statistics that are similar in location and timing, though not in magnitude, to those produced by the GCM. This is either a coincidence or evidence of feedback in nature. Central North America, of course, is just one of the Earth’s wet/dry transitions zones. Annual Precipitation Another is the Sahel… Does nature allow land-atmosphere feedback to affect rainfall statistics in the Sahel? Precipitation Variances (mm2/day2) AGCM The comparison between model results and observations isn’t as clear-cut as it is in North America, but it is suggestive… AGCM with no land feedback Observations Precipitation Variances (mm2/day2) AGCM The comparison between model results and observations isn’t as clear-cut as it is in North America, but it is suggestive… AGCM with no land feedback Observations The dots show where precipitation itself is maximized Another observational study If land-atmosphere feedback operates in the Sahel, then realistic land initialization there should lead to improved monthly forecasts. Test with comprehensive forecast study: 75 start dates (first days of each month: May to September) 9 ensemble members per forecast Compare In one set of forecasts, utilize realistic land ICs In other set, don’t utilize realistic land ICs Forecast skill resulting from realistic land surface initialization appears negligible for precipitation… Skill from knowing SST distribution and realistic land ICs Skill from knowing SST distribution Differences: Added forecast skill from realistic land ICs Precipitation Precipitation Precipitation Temperature Temperature Temperature HOWEVER, locations for which the rain gauge density is adequate enough to properly initialize the model are arguably very limited. Regions w/adequate raingauge density and model predictability Added forecast skill from land initialization Precipitation Temperature So, for the feedback question, observations are limited. Consider now a pure model study... # of Exp. simulations Length Total years A 4 200 yr 800 Prescribed, climatological land; climatological ocean AL 4 200 yr 800 Interactive land, climatological ocean AO 16 45 yr 720 Prescribed, climatological land, interannually varying ocean ALO 16 45 yr 720 Description Interactive land, interannually varying ocean Evaporation efficiency (ratio of evaporation to potential evaporation) prescribed at every time step to seasonally-varying climatological means SSTs set to seasonally-varying climatological means (from obs) SSTs set to interannually-varying values (from obs) LSM in model allowed to run freely Koster et al., J. Hydromet., 1, 26-46, 2000 Simulated precipitation variability can be described in terms of a simple linear system: Total precipitation variance Precipitation variance in the absence of land feedback s2ALO = s2AO [ Xo + ( 1 - Xo ) ] Fractional contribution of ocean processes to precipitation variance Fractional contribution of chaotic atmospheric dynamics to precipitation variance s2ALO s2AO Land-atmosphere feedback factor The above tautology isolates the relative contributions of SSTs, soil moisture, and chaotic atmospheric dynamics to precipitation variability. Contributions to Precipitation Variability Idealized “predictability” (for 1-month forecasts, MJJAS) deduced from aforementioned forecast experiment. (“Ability of model to predict itself.”) Predictability from SST distribution and realistic land ICs Predictability from SST distribution Differences: Added predictability from realistic land ICs Precipitation Precipitation Precipitation Temperature Temperature Temperature Temperature More AGCM results: The GLACE multi-model experiment. In GLACE, land-atmosphere feedback was quantified independently in 12 AGCMs. While the models differ in their feedback strengths, certain features of the coupling patterns are common amongst them. These features are brought out by averaging over all of the model results: More AGCM results: The GLACE multi-model experiment. In GLACE, land-atmosphere feedback was quantified independently in 12 AGCMs. While the models differ in their feedback strengths, certain features of the coupling patterns are common amongst them. These features are brought out by averaging over all of the model results: The AGCMs tend to agree: land-atmosphere feedback operates in the Sahel. To summarize: Organization of Talk 1. Overview of the processes that control land-atmosphere feedback. (Case study: North America) 2. Application of these ideas to the Sahel: do the observations support the existence of feedback there? 3. Model study of the controls on Sahelian rainfall variability. To summarize: Organization of Talk 1. Overview of the processes that control land-atmosphere feedback. (Case study: North America) We think we understand the impact 2. Application of these ideas to the Sahel: do the on the of land-atmosphere feedback observations support thestatistics existence of feedback there? of precipitation in North America. Through feedback, precipitation and variance 3. Model study of the controls on the memory West African are increased in the transition zones monsoon. between wet and dry areas. The observations appear to support this. To summarize: Organization of Talk 1. Overview of the processes that control land-atmosphere feedback. (Case study: North America) 2. Application of these ideas to the Sahel: do the observations support the existence of feedback there? Observations are too sparse in the Sahel 3. Model study of the controls ontothe West African (relative North America) for an equally monsoon. clear indication that land atmosphere feedback operates there. Nevertheless, the available observations are not inconsistent with feedback. To summarize: Organization of Talk 1. Overview of the processes that control land-atmosphere feedback. (Case study: North America) 2. Application of these ideas to the Sahel: do the observations support the existence of feedback there? 3. Model study of the controls on Sahelian rainfall variability. The NSIPP model (and indeed most of the models participating in GLACE) show the Sahel to be a region of strong landatmosphere feedback. The above modeling results may, of course, be model dependent. A new, upcoming experiment may provide a clearer look at the controls on monsoon dynamics… WAMME West African Monsoon Modeling and Evaluation See website: http://wamme.geog.ucla.edu/ A Spring AGU (Acapulco) session addresses the experiment… Experiment Design W Simulations: Establish a time series of surface conditions time step n Step forward the coupled AGCM-LSM time step n+1 Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Write the values of the land surface prognostic variables into file W1_STATES (Repeat without writing to obtain simulations W2 – W16) All simulations are run from June through August Experiment Design (cont.) R(S) Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of surface (deeper) prognostic variables time step n Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n from file W1_STATES time step n+1 Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from file W1_STATES Participating Groups Model 1. BMRC with CHASM 2. U. Tokyo w/ MATSIRO Contact McAvaney/Pitman Kanae/Oki Country 3. COLA with SSiB 4. CSIRO w/ 2 land schemes 5. NCAR Dirmeyer Kowalczyk Oleson USA Australia USA 6. Env. Canada with CLASS 7. GFDL with LM2p5 8. GSFC(GLA) with SSiB 9. Hadley Centre w/ MOSES2 10. NCEP/EMC with NOAH 11. NSIPP with Mosaic 12. UCLA with SSiB Verseghy Gordon Sud Taylor Lu/Mitchell Koster Xue Canada Australia Japan USA USA UK USA USA USA W: GFDL Scale goes from 0 to 1 S: GFDL Scale goes from 0 to 1 Differences: GFDL Scale goes from -0.5 to 0.5 Another pure model study (no observations): monsoon rainfall What controls the timing of the monsoon? Quantify importance of: 1. Average solar cycle. 2. Interannual SST variations 3. Interannual soil moisture variations Region considered Illustration of W diagnostic (not for African monsoon region) Precipitation time series produced by different ensemble members under the same forcing All simulations in ensemble respond similarly to boundary forcing W is high Simulations in ensemble have no coherent response to boundary forcing W is low The contributions of the different boundary forcings to the agreement (between ensemble members) of monsoon structure is established by analyzing the outputs of various experiments… NSIPP model W solar, solar, SSTs solar, SSTs, soil moisture solar, SSTs (Middle two bars differ because they were derived from different experiments, with different assumptions.) The contributions of the different boundary forcings to the agreement (between ensemble members) of monsoon structure is established by analyzing the outputs of various experiments… NSIPP model W In this model, soil moisture variations have a major impact on monsoon evolution solar, solar, SSTs solar, SSTs, soil moisture solar, SSTs (Middle two bars differ because they were derived from different experiments, with different assumptions.)