Jiming Jin

Simulation of high-latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2(e) 1: Experiment description and summary intercomparisons

Abstract Twenty-one land-surface schemes (LSSs) participated in the Project for Intercomparison of Land-surface Parameterizations (PILPS) Phase 2(e) experiment, which used data from the Torne–Kalix Rivers in northern Scandinavia. Atmospheric forcing data (precipitation, air temperature, specific humidity, wind speed, downward shortwave and longwave radiation) for a 20-year period (1979–1998) were provided to the 21 participating modeling groups for 218 1/4° grid cells that represented the study domain. The first decade (1979–1988) of the period was used for model spin-up. The quality of meteorologic forcing variables is of particular concern in high-latitude experiments and the quality of the gridded dataset was assessed to the extent possible. The lack of sub-daily precipitation, underestimation of true precipitation and the necessity to estimate incoming solar radiation were the primary data concerns for this study. The results from two of the three types of runs are analyzed in this, the first of a three-part paper: (1) calibration–validation runs—calibration of model parameters using observed streamflow was allowed for two small catchments (570 and 1300 km2), and parameters were then transferred to two other catchments of roughly similar size (2600 and 1500 km2) to assess the ability of models to represent ungauged areas elsewhere; and 2) reruns—using revised forcing data (to resolve problems with apparent underestimation of solar radiation of approximately 36%, and certain other problems with surface wind in the original forcing data). Model results for the period 1989–1998 are used to evaluate the performance of the participating land-surface schemes in a context that allows exploration of their ability to capture key processes spatially. In general, the experiment demonstrated that many of the LSSs are able to capture the limitations imposed on annual latent heat by the small net radiation available in this high-latitude environment. Simulated annual average net radiation varied between 16 and 40 W/m2 for the 21 models, and latent heat varied between 18 and 36 W/m2. Among-model differences in winter latent heat due to the treatment of aerodynamic resistance appear to be at least as important as those attributable to the treatment of canopy interception. In many models, the small annual net radiation forced negative sensible heat on average, which varied among the models between −11 and 9 W/m2. Even though the largest evaporation rates occur in the summer (June, July and August), model-predicted snow sublimation in winter has proportionately more influence on differences in annual runoff volume among the models. A calibration experiment for four small sub-catchments of the Torne–Kalix basin showed that model parameters that are typically adjusted during calibration, those that control storage of moisture in the soil column or on the land surface via ponding, influence the seasonal distribution of runoff, but have relatively little impact on annual runoff ratios. Similarly, there was no relationship between annual runoff ratios and the proportion of surface and subsurface discharge for the basin as a whole.

Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e): 2: Comparison of model results with observations

Model results from 21 land-surface schemes (LSSs) designed for use in numerical weather prediction and climate models are compared with each other and with observations in the context of the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(e) model intercomparison experiment. This experiment focuses on simulations of land-surface water and energy fluxes in the 58,000-km2 Torne and Kalix river systems in northern Scandinavia, during the period 1989–1998. All models participating in PILPS Phase 2(e) capture the broad dynamics of snowmelt and runoff, but large differences in snow accumulation and ablation, turbulent heat fluxes, and streamflow exist. The greatest among-model differences in energy and moisture fluxes in these high-latitude environments occur during the spring snowmelt period, reflecting different model parameterizations of snow processes. Differences in net radiation are governed by differences in the simulated radiative surface temperature during the winter months and by differences in surface albedo during the spring/early summer. Differences in net radiation are smallest during the late summer when snow is absent. Although simulated snow sublimation is small for most models, a few models show annual snow sublimation of about 100 mm. These differences in snow sublimation appear to be largely dependent on differences in snow surface roughness parameterizations. The models with high sublimation generally lose their snowpacks too early compared to observations and underpredict the annual runoff. Differences in runoff parameterizations are reflected in differences in daily runoff statistics. Although most models show a greater variability in daily streamflow than the observations, the models with the greatest variability (as much as double the observed variability), produce most of their runoff through fast response, surface runoff mechanisms. As a group, those models that took advantage of an opportunity to calibrate to selected small catchments and to transfer calibration results to the basin at large had a smaller bias and root mean squared error (RMSE) in daily streamflow simulations compared with the models that did not calibrate.

Climate, Extreme Heat, and Electricity Demand in California

Climate, Extreme Heat, and Electricity Demand in California Norman L. Miller 1* , Katharine Hayhoe 2 , Jiming Jin 1 , Maximilian Auffhammer 3 Earth Sciences Division, Berkeley National Laboratory, University of California, Berkeley, CA 94720 Department of Geosciences, Texas Tech University, Lubbock, TX 79409 Agricultural and Resource Economics Department, University of California, Berkeley * Atmosphere and Ocean Sciences Group, 1 Cyclotron Road, Berkeley, CA 94720 phone: 510.495.2374, fax: 510.486.5686, email: nlmiller@lbl.gov Submitted to the Journal of Applied Meteorology and Climatology on 17 April 2006 Revised and resubmitted on 25 October 2006

A simple snow‐atmosphere‐soil transfer model

This paper presents a simple snow model for climate studies. There are three prognostic variables in the model: specific enthalpy, snow water equivalent, and snow depth. This model is developed on the basis of up-to-date comprehensive and complex snow schemes but with substantial simplification and improvement. The effects of vapor on snow processes have been analyzed in the paper. On the basis of the analysis, vapors contribution in the mass equation is eliminated, and an effective conductivity coefficient, which includes a simple parameterization for vapor diffusion effect, is used to describe its contribution in the energy equation to simplify the computation. Specific enthalpy is used in the energy balance equation. Using enthalpy rather than temperature greatly simplifies the computational procedure for the phase change calculation in the snow process. This approach, along with a one-step test scheme that avoids iterations, saves computational time, which is important for general circulation model (GCM) simulations. The layering scheme is a critical part in the model. After many tests, it is found that three layers with an appropriate layering scheme are adequate for most cases. Preliminary testing using Russian and French snow data shows that the three-layer model is able to produce reasonable and consistent results.

Comparative Analyses of Physically Based Snowmelt Models for Climate Simulations

A comparative study of three snow models with different complexities was carried out to assess how a physically detailed snow model can improve snow modeling within general circulation models. The three models were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses the mixture theory to simulate multiphase water and energy transfer processes in snow layers; (b) a simplified three-layer model, Snow‐Atmosphere‐Soil Transfer (SAST), which includes only the ice and liquid-water phases; and (c) the snow submodel of the Biosphere‐Atmosphere Transfer Scheme (BATS), which calculates snowmelt from the energy budget and snow temperature by the force‐restore method. Given the same initial conditions and forcing of atmosphere and radiation, these three models simulated time series of snow water equivalent, surface temperature, and fluxes very well, with SNTHERM giving the best match with observations and SAST simulation being close. BATS captured the major processes in the upper portion of a snowpack where solar radiation provides the main energy source and gave satisfying results for seasonal periods. Some biases occurred in BATS surface temperature and energy exchange due to its neglecting of liquid water and underestimating snow density. Ice heat conduction, meltwater heat transport, and the melt‐freeze process of snow exhibit strong diurnal variations and large gradients at the uppermost layers of snowpacks. Using two layers in the upper 20 cm and one deeper layer at the bottom to simulate the multiphase snowmelt processes, SAST closely approximated the performance of SNTHERM with computational requirements comparable to those of BATS.

Sensitivity Study of Four Land Surface Schemes in the WRF Model

The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes.

Simulation of snow mass and extent in general circulation models

An evaluation of the Biosphere Atmosphere Transfer Scheme (BATS) snow submodel was conducted, both in a stand-alone mode and within the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3). We evaluated, in the stand-alone mode, the performance of BATS parameterizations at local scales using ground-based observations from the former Soviet Union and from Mammoth Mountain, California. The BATS snow scheme reproduces well the seasonal evolution of snow water equivalent in both sites, and the results for the Mammoth Mountain site compare well with those from a more complex, physically based model (SNTHERM), In the coupled mode, we evaluated the modelled snow cover extent, snow mass, precipitation and temperature from BATS as linked to the NCAR CCM3 using available observations. The coupled models capture the broad pattern of seasonal and geographical distribution of snow cover, with better overall performance than the passive microwave snow data derived from the Nimbus-7 Scanning Multi-channel Microwave Radiometer (SMMR) which generally underestimates snow depth. In terms of continents, the snow mass is better simulated during the accumulation period than during the melt period, which is the case for both North America and Eurasia, The simulation of snow mass, precipitation and air temperature for North America is slightly better than that for Eurasia. A rigorous evaluation of snow simulations in coupled land-atmosphere models requires high quality global datasets of snow cover extent, snow depth and snow water equivalent. The available datasets and model outputs are not yet ready to fulfil this objective.

Analysis of the Impact of Snow on Daily Weather Variability in Mountainous Regions Using MM5

The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, California–Nevada, mountainous region, were studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forced by 6-h reanalysis data from the National Centers for Environmental Prediction. The analysis of two-way nested 36–12-km MM5 simulations during the 1998 snowmelt season (April–June) shows that the snow water equivalent (SWE) is underestimated when there are conditions with higher temperature and greater precipitation than observations. An observed daily SWE dataset derived from the snow telemetry network was assimilated into the Noah land surface model within MM5. This SWE assimilation reduces the warm bias. The reduction of the warm bias results from suppressed upward sensible heat flux caused by the decreased skin temperature. This skin temperature reduction is the result of the longer assimilated snow duration than in the model run without SWE assimilation. Meanwhile, the cooled surface leads to a more stable atmosphere, resulting in a decrease in the exaggerated precipitation. Additionally, the detailed analysis of the snowmelt indicates that the absence of vegetation fraction in the most sophisticated land surface model (Noah) in the MM5 package results in an overestimation of solar radiation reaching the snow surface, giving rise to heavier snowmelt. An underestimated surface albedo also weakly contributes to the stronger snowmelt. The roles of the vegetation fraction and albedo in snowmelt are further verified by an additional offline simulation from a more realistic land surface model with advanced snow and vegetation schemes forced by the MM5 output. An improvement in SWE description is clearly seen in this offline simulation over the Sierra Nevada region.

Integrating Remote Sensing Data with WRF for Improved Simulations of Oasis Effects on Local Weather Processes over an Arid Region in Northwestern China

AbstractLand use/cover types derived by satellite remote sensing data from the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) were used to replace the U.S. Geological Survey (USGS) data in the Weather Research and Forecasting Model (WRF). Simulations in this study were further improved by modifying the initial fields of WRF with soil temperature and moisture observations, because these two variables are important to producing “cold–wet island” effects. A series of WRF simulations were performed to describe microclimate characteristics and the local thermal circulation generated by the inhomogeneous surface over the Jinta oasis, which is located in Gansu—a northwestern province of China. Comparison between simulations and observations showed that the WRF results produced with observed soil temperature and moisture initializations agreed well with near-surface measurements of air temperature, relative humidity, and wind direction. Moreover, low temperatures over the oasis were ...

Systematic patterns of the inconsistency between snow water equivalent and accumulated precipitation as reported by the Snowpack Telemetry Network

AbstractThe authors investigated the accuracy of snow water equivalent (SWE) observations compiled by 748 Snowpack Telemetry stations and attributed the systematic bias introduced to SWE measurements to drifting snow. Often observed, SWE outpaces accumulated precipitation (AP), which can be statistically and physically explained through 1) precipitation undercatchment and/or 2) drifting snow. Forty-four percent of the 748 stations reported at least one year where the maximum SWE was greater than AP, while 16% of the stations showed this inconsistency for at least 20% of the observed years. Regions with a higher likelihood of inconsistency contained drier snow and are exposed to higher winds speeds, both of which are positively correlated to drifting snow potential as well as gauge undercatch. Differentiating between gauge undercatch and potential drifting scenarios, days when SWE increased but AP remained zero were used. These drift days occurred on an average of 13.3 days per year for all stations, with ...