Search

Sea surface temperature (SST) variability has been shown to have predictive value for land precipitation, although SSTs are unable to fully predict intraseasonal to interannual hydrologic extremes. The possible remote effects of large-scale land surface temperature (LST) and subsurface temperature (SUBT) anomalies in geographical areas upstream and closer to the areas of drought/flood have largely been ignored. Here evidence from climate observations and model simulations addresses these effects. Evaluation of observational data using Maximum Covariance Analysis identifies significant correlations between springtime 2-m air temperature (T2 m) cold (warm) anomalies in both the western U.S. and the Tibetan Plateau and downstream drought (flood) events in late spring/summer. To support these observational findings, climate models are used in sensitivity studies, in which initial LST/SUBT anomaly is imposed to produce observed T2 m anomaly, to demonstrate a causal relationship for two important cases: between spring warm T2 m/LST/SUBT anomalies in western U.S. and the extraordinary 2015 flood in Southern Great Plains and adjacent regions and between spring cold T2 m/LST/SUBT anomalies in the Tibetan Plateau and the severe 2003 drought south of the Yangtze River region. The LST/SUBT downstream effects in North America are associated with a large-scale atmospheric stationary wave extending eastward from the LST/SUBT anomaly region. The effects of SST in these cases are also tested and compared with the LST/SUBT effects. These results suggest that consideration of LST/SUBT anomalies has the potential to add value to intraseasonal prediction of dry and wet conditions, especially extreme drought/flood events. The results suggest the importance of developing land data and models capable of preserving observed soil memory.

Two important factors that control snow albedo are snow aging (grain growth) and presence of light-absorbing impurities (aerosols) in snow. However, most current regional climate models do not include such processes in a physically-based manner in their land surface models, which can have serious implications to simulated surface energy and water budgets, and ultimately to water resources and regional climate. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the SNow ICe And Radiative (SNICAR) scheme. SNICAR is a snow radiative transfer model that simulates snow albedo evolution due to snow grain growth and presence of aerosols in snow in a physically-based manner. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. The newly modified SSiB-3 land surface model (LSM) is validated offline with in situ observations at a location in Western U.S. (WUS), and shows significant improvements in simulated snow albedo and depth when dust in snow is considered, and reproduces snow grain size and vertical distribution of dust in snow that are comparable to those observed. The online, coupled version of the new model, WRF/SSiB-3/aer, is then employed to conduct two 10-year long simulations over North America - aerosols-loaded (AER) snow and clean snow (NOAER) cases - to investigate the impact of aerosols in snow (AIS) on surface energy (SEB) and water (WB) budgets on a regional scale. GOCART surface aerosol deposition data is used in AER scenario. Comparisons between AER and NOAER simulations reveal albedo is reduced the most during the ablation period in the presence of AIS, inducing a surface radiative forcing (RF) ranging 8.5 W/m2 over WUS to 13.6 W/m2 over NCan during MAM and MJJ (10-year means), respectively, but as high as 65 W/m2 during

Two important factors that control snow albedo are snow aging (grain growth) and presence of light-absorbing impurities (aerosols) in snow. However, most current regional climate models do not include such processes in a physically-based manner in their land surface models, which can have serious implications to simulated surface energy and water budgets, and ultimately to water resources and regional climate. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the SNow ICe And Radiative (SNICAR) scheme. SNICAR is a snow radiative transfer model that simulates snow albedo evolution due to snow grain growth and presence of aerosols in snow in a physically-based manner. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. The newly modified SSiB-3 land surface model (LSM) is validated offline with in situ observations at a location in Western U.S. (WUS), and shows significant improvements in simulated snow albedo and depth when dust in snow is considered, and reproduces snow grain size and vertical distribution of dust in snow that are comparable to those observed. The online, coupled version of the new model, WRF/SSiB-3/aer, is then employed to conduct two 10-year long simulations over North America - aerosols-loaded (AER) snow and clean snow (NOAER) cases - to investigate the impact of aerosols in snow (AIS) on surface energy (SEB) and water (WB) budgets on a regional scale. GOCART surface aerosol deposition data is used in AER scenario. Comparisons between AER and NOAER simulations reveal albedo is reduced the most during the ablation period in the presence of AIS, inducing a surface radiative forcing (RF) ranging 8.5 W/m2 over WUS to 13.6 W/m2 over NCan during MAM and MJJ (10-year means), respectively, but as high as 65 W/m2 during

Two important factors that control snow albedo are snow aging (grain growth) and presence of light-absorbing impurities (aerosols) in snow. However, most current regional climate models do not include such processes in a physically-based manner in their land surface models, which can have serious implications to simulated surface energy and water budgets, and ultimately to water resources and regional climate. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the SNow ICe And Radiative (SNICAR) scheme. SNICAR is a snow radiative transfer model that simulates snow albedo evolution due to snow grain growth and presence of aerosols in snow in a physically-based manner. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. The newly modified SSiB-3 land surface model (LSM) is validated offline with in situ observations at a location in Western U.S. (WUS), and shows significant improvements in simulated snow albedo and depth when dust in snow is considered, and reproduces snow grain size and vertical distribution of dust in snow that are comparable to those observed. The online, coupled version of the new model, WRF/SSiB-3/aer, is then employed to conduct two 10-year long simulations over North America - aerosols-loaded (AER) snow and clean snow (NOAER) cases - to investigate the impact of aerosols in snow (AIS) on surface energy (SEB) and water (WB) budgets on a regional scale. GOCART surface aerosol deposition data is used in AER scenario. Comparisons between AER and NOAER simulations reveal albedo is reduced the most during the ablation period in the presence of AIS, inducing a surface radiative forcing (RF) ranging 8.5 W/m2 over WUS to 13.6 W/m2 over NCan during MAM and MJJ (10-year means), respectively, but as high as 65 W/m2 during

Two important factors that control snow albedo are snow aging (grain growth) and presence of light-absorbing impurities (aerosols) in snow. However, most current regional climate models do not include such processes in a physically-based manner in their land surface models, which can have serious implications to simulated surface energy and water budgets, and ultimately to water resources and regional climate. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the SNow ICe And Radiative (SNICAR) scheme. SNICAR is a snow radiative transfer model that simulates snow albedo evolution due to snow grain growth and presence of aerosols in snow in a physically-based manner. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. The newly modified SSiB-3 land surface model (LSM) is validated offline with in situ observations at a location in Western U.S. (WUS), and shows significant improvements in simulated snow albedo and depth when dust in snow is considered, and reproduces snow grain size and vertical distribution of dust in snow that are comparable to those observed. The online, coupled version of the new model, WRF/SSiB-3/aer, is then employed to conduct two 10-year long simulations over North America - aerosols-loaded (AER) snow and clean snow (NOAER) cases - to investigate the impact of aerosols in snow (AIS) on surface energy (SEB) and water (WB) budgets on a regional scale. GOCART surface aerosol deposition data is used in AER scenario. Comparisons between AER and NOAER simulations reveal albedo is reduced the most during the ablation period in the presence of AIS, inducing a surface radiative forcing (RF) ranging 8.5 W/m2 over WUS to 13.6 W/m2 over NCan during MAM and MJJ (10-year means), respectively, but as high as 65 W/m2 during

Two important factors that control snow albedo are snow aging (grain growth) and presence of light-absorbing impurities (aerosols) in snow. However, most current regional climate models do not include such processes in a physically-based manner in their land surface models, which can have serious implications to simulated surface energy and water budgets, and ultimately to water resources and regional climate. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the SNow ICe And Radiative (SNICAR) scheme. SNICAR is a snow radiative transfer model that simulates snow albedo evolution due to snow grain growth and presence of aerosols in snow in a physically-based manner. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. The newly modified SSiB-3 land surface model (LSM) is validated offline with in situ observations at a location in Western U.S. (WUS), and shows significant improvements in simulated snow albedo and depth when dust in snow is considered, and reproduces snow grain size and vertical distribution of dust in snow that are comparable to those observed. The online, coupled version of the new model, WRF/SSiB-3/aer, is then employed to conduct two 10-year long simulations over North America - aerosols-loaded (AER) snow and clean snow (NOAER) cases - to investigate the impact of aerosols in snow (AIS) on surface energy (SEB) and water (WB) budgets on a regional scale. GOCART surface aerosol deposition data is used in AER scenario. Comparisons between AER and NOAER simulations reveal albedo is reduced the most during the ablation period in the presence of AIS, inducing a surface radiative forcing (RF) ranging 8.5 W/m2 over WUS to 13.6 W/m2 over NCan during MAM and MJJ (10-year means), respectively, but as high as 65 W/m2 during