World Weather Open Science – 17 August 2014 The Impact of Land Surface on Sub-seasonal Forecast Skill Zhichang Guo and Paul Dirmeyer Center for Ocean-Land-Atmosphere Studies George Mason University, Fairfax, USA

World Weather Open Science – 17 August 2014 Soil moisture is a potentially critical component of subseasonal to seasonal prediction system, especially over continental midlatitude areas where SST has limited impacts on; Previous studies of land-atmosphere interaction and land surface impacts on atmospheric prediction have been limited by a lack of observational data and by the model dependence of the results; Background

World Weather Open Science – 17 August 2014 Recently, two multi-institutional numerical modeling experiments, GLACE-1 and GLACE-2 (Global Land- Atmosphere Coupling Experiments) have been designed and performed to study systematically impacts of land surface on atmospheric variability and its prediction; Advantages: it allows to synthesize commonalities among various models which are less subject to problems in the process parameterizations and allows the inter-model comparison. GLACE-1 and GLACE-2

World Weather Open Science – 17 August GFS/OSU, NCEP, USA; 2.GEOS, NASA/GSFC, USA; 3.GFDL, USA; 4.CAM/CLM, NCAR, USA; 5.CCCma, Canada; 6.HadAm3, UK; 7.BMRC, Australia; 8.CSIRO-CC3, Australia; 9.CCSR, Japan; 10. UCAL, USA 11.NSIPP, NASA/GSFC, USA; 12.COLA AGCM, COLA/GMU, USA; 12 models 1.NCEP GCM, Princeton University, USA; 2.GEOS, NASA/GSFC, USA; 3.GFDL, USA; 4.CAM/CLM, NCAR, U. Gothenburg, Sweden; 5.ECMWF; 6.KNMI, Netherlands, ECMWF; 7.ECHAM GCM, IACS, Switzerland; 8.CCCma, Canada; 9.NSIPP, NASA/GSFC, USA; 10. COLA AGCM, COLA/GMU, USA; 11. FSU/COAPS. FSU, USA GLACE-1 Models GLACE-2 Models

World Weather Open Science – 17 August 2014 A question asked in GLACE-1: do changes in soil moisture anomaly have impacts on atmospheric variability? (commonalities among various models and inter-model comparison). Obviously, if changes in soil moisture anomaly are being ignored by the atmosphere, it won’t contribute to the atmospheric prediction skills. GLACE-1: Does atmosphere care about anomalies in soil moisture?

World Weather Open Science – 17 August 2014 Hotspots: regions with strong coupling strength where 12 participating models are relatively consistent This figure received extensive attention due to the following reasons: 1. It is found soil moisture has strong impacts on atmospheric variability over some areas, namely hotspots, ; 2. Hopefully, realistic soil moisture initialization could improve significantly subseasonal to seasonal prediction over these areas. This figure received extensive attention due to the following reasons: 1. It is found soil moisture has strong impacts on atmospheric variability over some areas, namely hotspots, ; 2. Hopefully, realistic soil moisture initialization could improve significantly subseasonal to seasonal prediction over these areas.

World Weather Open Science – 17 August 2014 If soil moisture has impacts on atmospheric variability, the realistic initialization of soil moisture should contribute to the subseasonal to seasonal forecast skill. Main topic of GLACE-2; Will the stronger L-A coupling result in a larger contribution to the subseasonal forecast skill? whether soil moisture over hotspots contribute significantly to the subseasonal to seasonal forecast? Topic of the GLACE-2

World Weather Open Science – 17 August 2014 Perform ensembles of retrospective seasonal forecasts realistic initial land surface states Prescribed, observed SSTs realistic initial atmospheric states Evaluate forecasts against observations Series 1: Perform ensembles of retrospective seasonal forecasts realistic initial land surface states Prescribed, observed SSTs realistic initial atmospheric states Evaluate forecasts against observations Series 2: “Randomize” land initialization! GLACE-2: Experiment Overview

World Weather Open Science – 17 August 2014 Forecast skill of air temperature （ July, )

World Weather Open Science – 17 August 2014 Commonalities: realistic initialization soil moisture contributes most over Southwestern and northern USA; The contribution is insignificant over the Great Plains (one of the hotspots). Why? stronger L-A coupling does not guarantee a larger contribution to the subseasonal forecast skill. Our explanation: stronger L-A coupling can result in a larger contribution to the subseasonal forecast skill only if the soil moisture can be predicted properly. In fact, forecast skill over hotspots are vulnerable since the model biases in the land surface there could be amplified into the atmosphere through strong L-A coupling.

World Weather Open Science – 17 August 2014 Soil moisture forecast skill and the role of soil moisture memory Soil moisture forecast skill heavily relies on initial quality of soil moisture. As the lead time increases, it is more dependent on soil moisture memory Soil moisture memory: how long does the soil moisture remember its anomaly into the future

World Weather Open Science – 17 August 2014 Over hotspots, forecast skill of air temperature is low due to lack of forecast skill in soil moisture, though land-atmosphere coupling is strong there Forecast skill of temperature and soil moisture Forecast skill of air temperature is high only over the areas where L-A coupling is strong and the forecast skill of soil moisture is large

World Weather Open Science – 17 August 2014 Forecast skill of air temperature is high when the product of forecast skill in soil moisture and land-atmosphere coupling strength is large Forecast skill of air temperature

World Weather Open Science – 17 August 2014 Comparison between Region A and Region B

World Weather Open Science – 17 August 2014 Inter-model comparison: forecast skill of temperature

World Weather Open Science – 17 August 2014 Inter-model comparison: forecast skill of soil moisture

World Weather Open Science – 17 August 2014 Inter-model difference in SM forecast skill explains inter-model difference in temperature forecast skill

World Weather Open Science – 17 August 2014 Forecast skill of soil moisture relies on the quality of soil moisture initialization and persistence of the soil moisture anomaly; Subseasonal forecast skill of temperature relies on the accurate prediction of soil moisture and strong L-A coupling; Realistic initialization of soil moisture contributes most to the subseasonal forecast over the Southwestern USA due to relatively strong land-atmosphere coupling and the high prediction skill of soil moisture there; its contribution to subseasonal predication of temperature is insignificant over the Great Plains due to the relatively low soil moisture prediction skill, even though the L-A coupling is strong there; The inter-model differences in temperature forecast skills could be explained by the inter-model difference in soil moisture forecast skills. Conclusion

World Weather Open Science – 17 August 2014 Thank You!