The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields, as simulations at higher spatial resolution are more capable of resolving the interaction between the topography and the atmosphere. However, most physics parameterizations (e.g. microphysics, cumulus, planetary boundary layer and land surface) are designed for grid spacings of 1 km or more, what complicates high-resolution modeling around this critical resolution. In this study, the WRF (Weather Research and Forecasting) model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation in the Nepalese Himalayas. Four model nests are set up with grid spacings of 25, 5, 1 and 0.5 km, respectively, and a typical summer (18th to 28th of July 2014) and winter (10th to 20th of February 2014) period are simulated. Simulated precipitation fields are compared with observational data obtained from automatic weather stations, pluviometers and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the observational data due to under catch of snowfall, the highest resolution of 500 meter provides the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias caused by the ERA-Interim data is decreased

The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields, as simulations at higher spatial resolution are more capable of resolving the interaction between the topography and the atmosphere. However, most physics parameterizations (e.g. microphysics, cumulus, planetary boundary layer and land surface) are designed for grid spacings of 1 km or more, what complicates high-resolution modeling around this critical resolution. In this study, the WRF (Weather Research and Forecasting) model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation in the Nepalese Himalayas. Four model nests are set up with grid spacings of 25, 5, 1 and 0.5 km, respectively, and a typical summer (18th to 28th of July 2014) and winter (10th to 20th of February 2014) period are simulated. Simulated precipitation fields are compared with observational data obtained from automatic weather stations, pluviometers and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the observational data due to under catch of snowfall, the highest resolution of 500 meter provides the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias caused by the ERA-Interim data is decreased

The Impact of Spatial Resolution, Land Use, and Spinup Time on Resolving Spatial Precipitation Patterns in the Himalayas

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalayas because of their relatively low spatial resolution and the associated representation of the complex topography. Dy- namical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields. However, most physical parameterization schemes are designed for a spatial resolution coarser than 1 km. In this study the Weather Research and Forecasting (WRF) Model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation, 2-m temperature, and wind fields in the Nepalese Himalayas. Four model nests are set up with spatial resolutions of 25, 5, 1, and 0.5 km, respectively, and a typical 10-day period in summer and winter in 2014 are simulated. The model output is compared with observational data obtained from automatic weather stations, pluviometers, and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the obser- vational data due to undercatch of snowfall, the highest resolution of 500 m does provide the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias is decreased. Our results further elucidate the performance of WRF at high resolution and demonstrate the importance of accurate surface boundary conditions and spinup time for simulating precipitation. Furthermore, they suggest that future modeling studies of High Mountain Asia should consider a subkilometer grid for accurately estimating local meteorological variability

The Impact of Spatial Resolution, Land Use, and Spinup Time on Resolving Spatial Precipitation Patterns in the Himalayas

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalayas because of their relatively low spatial resolution and the associated representation of the complex topography. Dy- namical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields. However, most physical parameterization schemes are designed for a spatial resolution coarser than 1 km. In this study the Weather Research and Forecasting (WRF) Model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation, 2-m temperature, and wind fields in the Nepalese Himalayas. Four model nests are set up with spatial resolutions of 25, 5, 1, and 0.5 km, respectively, and a typical 10-day period in summer and winter in 2014 are simulated. The model output is compared with observational data obtained from automatic weather stations, pluviometers, and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the obser- vational data due to undercatch of snowfall, the highest resolution of 500 m does provide the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias is decreased. Our results further elucidate the performance of WRF at high resolution and demonstrate the importance of accurate surface boundary conditions and spinup time for simulating precipitation. Furthermore, they suggest that future modeling studies of High Mountain Asia should consider a subkilometer grid for accurately estimating local meteorological variability

Contrasting Meteorological Drivers of the Glacier Mass Balance Between the Karakoram and Central Himalaya

There is strong variation in glacier mass balances in High Mountain Asia. Particularly interesting is the fact that glaciers are in equilibrium or even gaining mass in the Karakoram and Kunlun Shan ranges, which is in sharp contrast with the negative mass balances in the rest of High Mountain Asia. To understand this difference, an in-depth understanding of the meteorological drivers of the glacier mass balance is required. In this study, two catchments in contrasting climatic regions, one in the central Himalaya (Langtang) and one in the Karakoram (Shimshal), are modeled at 1 km grid spacing with the numerical atmospheric model WRF for the period of 2011–2013. Our results show that the accumulation and melt dynamics of both regions differ due to contrasting meteorological conditions. In Shimshal, 92% of the annual precipitation falls in the form of snow, in contrast with 42% in Langtang. In addition, 80% of the total snow falls above an altitude of 5000 m a.s.l, compared with 35% in Langtang. Another prominent contrast is that most of the annual snowfall falls between December and May (71%), compared with 52% in Langtang. The melt regimes are also different, with 41% less energy available for melt in Shimshal. The melt in the Karakoram is controlled by net shortwave radiation (r = 0.79 ± 0.01) through the relatively low glacier albedo in summer, while net longwave radiation (clouds) dominates the energy balance in the Langtang region (r = 0.76 ± 0.02). High amounts of snowfall and low melt rates result in a simulated positive glacier surface mass balance in Shimshal (+0.31 ± 0.06 m w.e. year−1) for the study period, while little snowfall, and high melt rates lead to a negative mass balance in Langtang (−0.40 ± 0.09 m w.e. year−1). The melt in Shimshal is highly variable between years, and is especially sensitive to summer snow events that reset the surface albedo. We conclude that understanding glacier mass balance anomalies requires quantification and insight into subtle shifts in the energy balance and accumulation regimes at high altitude and that the sensitivity of glaciers to climate change is regionally variable

Contrasting Meteorological Drivers of the Glacier Mass Balance Between the Karakoram and Central Himalaya

There is strong variation in glacier mass balances in High Mountain Asia. Particularly interesting is the fact that glaciers are in equilibrium or even gaining mass in the Karakoram and Kunlun Shan ranges, which is in sharp contrast with the negative mass balances in the rest of High Mountain Asia. To understand this difference, an in-depth understanding of the meteorological drivers of the glacier mass balance is required. In this study, two catchments in contrasting climatic regions, one in the central Himalaya (Langtang) and one in the Karakoram (Shimshal), are modeled at 1 km grid spacing with the numerical atmospheric model WRF for the period of 2011–2013. Our results show that the accumulation and melt dynamics of both regions differ due to contrasting meteorological conditions. In Shimshal, 92% of the annual precipitation falls in the form of snow, in contrast with 42% in Langtang. In addition, 80% of the total snow falls above an altitude of 5000 m a.s.l, compared with 35% in Langtang. Another prominent contrast is that most of the annual snowfall falls between December and May (71%), compared with 52% in Langtang. The melt regimes are also different, with 41% less energy available for melt in Shimshal. The melt in the Karakoram is controlled by net shortwave radiation (r = 0.79 ± 0.01) through the relatively low glacier albedo in summer, while net longwave radiation (clouds) dominates the energy balance in the Langtang region (r = 0.76 ± 0.02). High amounts of snowfall and low melt rates result in a simulated positive glacier surface mass balance in Shimshal (+0.31 ± 0.06 m w.e. year−1) for the study period, while little snowfall, and high melt rates lead to a negative mass balance in Langtang (−0.40 ± 0.09 m w.e. year−1). The melt in Shimshal is highly variable between years, and is especially sensitive to summer snow events that reset the surface albedo. We conclude that understanding glacier mass balance anomalies requires quantification and insight into subtle shifts in the energy balance and accumulation regimes at high altitude and that the sensitivity of glaciers to climate change is regionally variable

Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier

Debris-covered glaciers account for almost one-fifth of the total glacier ice volume in High Mountain Asia; however, their contribution to the total glacier melt remains uncertain, and the drivers controlling this melt are still largely unknown. Debris influences the properties (e.g. albedo, thermal conductivity, roughness) of the glacier surface and thus the surface energy balance and glacier melt. In this study we have used sensitivity tests to assess the effect of surface properties of debris on the spatial distribution of micrometeorological variables such as wind fields, moisture and temperature. Subsequently we investigated how those surface properties drive the turbulent fluxes and eventually the conductive heat flux of a debris-covered glacier. We simulated a debris-covered glacier (Lirung Glacier, Nepal) at a 1 m resolution with the MicroHH model, with boundary conditions retrieved from an automatic weather station (temperature, wind and specific humidity) and unmanned aerial vehicle flights (digital elevation map and surface temperature). The model was validated using eddy covariance data. A sensitivity analysis was then performed to provide insight into how heterogeneous surface variables control the glacier microclimate. Additionally, we show that ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface. The high spatial variability of small-scale meteorological variables suggests that point-based station observations cannot be simply extrapolated to an entire glacier. These outcomes should be considered in future studies for a better estimation of glacier melt in High Mountain Asia

Using large ensemble modelling to derive future changes in mountain specific climate indicators in a 2 and 3°C warmer world in High Mountain Asia

Natural disasters in High Mountain Asia (HMA) are largely induced by precipitation and temperatures extremes. Precipitation extremes will change due to global warming, but these low frequency events are difficult to analyse using (short) observed time series. In this study, we analysed large 2000 year ensembles of present day climate and of a 2 and 3°C warmer world produced with the EC‐Earth model. We performed a regional assessment of climate indicators related to temperature and precipitation (positive degree days, accumulated precipitation, [pre‐ and post‐] monsoon precipitation), their sensitivity to temperature change and the change in return periods of extreme temperature and precipitation in a 2 and 3°C warmer climate. In general, the 2°C warmer world shows a homogeneous response of changes in climate indicators and return periods, while distinct differences between regions are present in a 3°C warmer world and changes no longer follow a general trend. This non‐linear effect can indicate the presence of a tipping point in the climate system. The most affected regions are located in monsoon‐dominated regions, where precipitation amounts, positive degree days, extreme temperature, extreme precipitation and compound events are projected to increase the most. Largest changes in climate indicators are found in East Himalaya, followed by the Hindu Kush and West and Central Himalaya regions. Western regions will experience drier summers and wetter winters, while monsoon dominated regions drier winters and wetter summers and northern regions a wetter climate year round. We also found that precipitation increases in HMA in a 3°C warmer world are substantially larger (13%) compared to the global average (5.9%). Additionally, the increase in weather extremes will exacerbate natural hazards with large possible impacts for mountain communities. The results of this study could provide important guidance for formulating climate change adaptation strategies in HMA

Towards understanding the pattern of glacier mass balances in High Mountain Asia using regional climatic modelling

Glaciers in High Mountain Asia (HMA) provide an important water resource for communities downstream, and they are markedly impacted by global warming, yet there is a lack of understanding of the observed glacier mass balances and their spatial variability. In particular, the glaciers in the western Kunlun Shan and Karakoram (WKSK) ranges show neutral to positive mass balances despite global warming. Using models of the regional climate and glacier mass balance, we reproduce the observed patterns of glacier mass balance in High Mountain Asia of the last decades within uncertainties. We show that low temperature sensitivities of glaciers and an increase in snowfall, for a large part caused by increases in evapotranspiration from irrigated agriculture, result in positive mass balances in the WKSK. The pattern of mass balances in High Mountain Asia can thus be understood from the combination of changes in climatic forcing and glacier properties, with an important role for irrigated agriculture

Using large ensemble modelling to derive future changes in mountain specific climate indicators in a 2 and 3°C warmer world in High Mountain Asia

Natural disasters in High Mountain Asia (HMA) are largely induced by precipitation and temperatures extremes. Precipitation extremes will change due to global warming, but these low frequency events are difficult to analyse using (short) observed time series. In this study, we analysed large 2000 year ensembles of present day climate and of a 2 and 3°C warmer world produced with the EC‐Earth model. We performed a regional assessment of climate indicators related to temperature and precipitation (positive degree days, accumulated precipitation, [pre‐ and post‐] monsoon precipitation), their sensitivity to temperature change and the change in return periods of extreme temperature and precipitation in a 2 and 3°C warmer climate. In general, the 2°C warmer world shows a homogeneous response of changes in climate indicators and return periods, while distinct differences between regions are present in a 3°C warmer world and changes no longer follow a general trend. This non‐linear effect can indicate the presence of a tipping point in the climate system. The most affected regions are located in monsoon‐dominated regions, where precipitation amounts, positive degree days, extreme temperature, extreme precipitation and compound events are projected to increase the most. Largest changes in climate indicators are found in East Himalaya, followed by the Hindu Kush and West and Central Himalaya regions. Western regions will experience drier summers and wetter winters, while monsoon dominated regions drier winters and wetter summers and northern regions a wetter climate year round. We also found that precipitation increases in HMA in a 3°C warmer world are substantially larger (13%) compared to the global average (5.9%). Additionally, the increase in weather extremes will exacerbate natural hazards with large possible impacts for mountain communities. The results of this study could provide important guidance for formulating climate change adaptation strategies in HMA