scholarly journals Decomposition of Uncertainties between Coarse MM5–Noah-Simulated and Fine ASAR-Retrieved Soil Moisture over Central Tibet

2012 ◽  
Vol 13 (6) ◽  
pp. 1925-1938 ◽  
Author(s):  
Rogier van der Velde ◽  
Mhd. Suhyb Salama ◽  
Marcel D. van Helvoirt ◽  
Zhongbo Su ◽  
Yaoming Ma
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Scale ◽  
Large Scale ◽  
Climate Model ◽  
Mesoscale Model ◽  
The Other ◽  
Cumulative Distribution ◽  
Scale Difference ◽  
Central Tibet

Abstract Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.

A High-Resolution Climate Model for the U.S. Pacific Northwest: Mesoscale Feedbacks and Local Responses to Climate Change*

2008 ◽  
Vol 21 (21) ◽  
pp. 5708-5726 ◽  
Author(s):  
Eric P. Salathé ◽  
Richard Steed ◽  
Clifford F. Mass ◽  
Patrick H. Zahn
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Snow Cover ◽  
Pacific Northwest ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Model ◽  
Temperature And Precipitation

Abstract Simulations of future climate scenarios produced with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than in the global model in which it is nested, apparently because of mesoscale processes not being resolved at coarse resolution. Present-day (1990–99) and future (2020–29, 2045–54, and 2090–99) conditions are simulated at high resolution (15-km grid spacing) using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) system and forced by ECHAM5 global simulations. Simulations use the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emissions scenario, which assumes a rapid increase in greenhouse gas concentrations. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land–water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified through snow–albedo feedback in regions where snow cover is lost. Increased onshore flow in the spring reduces the daytime warming along the coast. Precipitation increases in autumn are amplified over topography because of changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results.

High-Resolution Climate Change Impact Analysis on Expected Future Water Availability in the Upper Jordan Catchment and the Middle East

2014 ◽  
Vol 15 (4) ◽  
pp. 1517-1531 ◽  
Author(s):  
Gerhard Smiatek ◽  
Harald Kunstmann ◽  
Andreas Heckl
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Water Availability ◽  
Impact Analysis ◽  
River Flow ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Climate Model ◽  
Full Range ◽  
The Impact

Abstract The impact of climate change on the future water availability of the upper Jordan River (UJR) and its tributaries Dan, Snir, and Hermon located in the eastern Mediterranean is evaluated by a highly resolved distributed approach with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) run at 18.6- and 6.2-km resolution offline coupled with the Water Flow and Balance Simulation Model (WaSiM). The MM5 was driven with NCEP reanalysis for 1971–2000 and with Hadley Centre Coupled Model, version 3 (HadCM3), GCM forcings for 1971–2099. Because only one regional–global climate model combination was applied, the results may not give the full range of possible future projections. To describe the Dan spring behavior, the hydrological model was extended by a bypass approach to allow the fast discharge components of the Snir to enter the Dan catchment. Simulation results for the period 1976–2000 reveal that the coupled system was able to reproduce the observed discharge rates in the partially karstic complex terrain to a reasonable extent with the high-resolution 6.2-km meteorological input only. The performed future climate simulations show steadily rising temperatures with 2.2 K above the 1976–2000 mean for the period 2031–60 and 3.5 K for the period 2070–99. Precipitation trends are insignificant until the middle of the century, although a decrease of approximately 12% is simulated. For the end of the century, a reduction in rainfall ranging between 10% and 35% can be expected. Discharge in the UJR is simulated to decrease by 12% until 2060 and by 26% until 2099, both related to the 1976–2000 mean. The discharge decrease is associated with a lower number of high river flow years.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

scholarly journals The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

2018 ◽  
Vol 18 (11) ◽  
pp. 2991-3006 ◽  
Author(s):  
Matthew D. K. Priestley ◽  
Helen F. Dacre ◽  
Len C. Shaffrey ◽  
Kevin I. Hodges ◽  
Joaquim G. Pinto
Keyword(s):  
High Resolution ◽  
Return Period ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Geographic Region ◽  
Near Surface ◽  
Short Period

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

Response of Seasonal Simulations of a Regional Climate Model to High-Frequency Variability of Soil Moisture during the Summers of 1988 and 1993

2007 ◽  
Vol 8 (4) ◽  
pp. 738-757 ◽  
Author(s):  
Song Yang ◽  
S-H. Yoo ◽  
R. Yang ◽  
K. E. Mitchell ◽  
H. van den Dool ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Vapor ◽  
Diurnal Cycle ◽  
High Frequency ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Water Vapor Transport ◽  
Diurnal Variability ◽  
High Frequency Variability

Abstract This study employs the NCEP Eta Regional Climate Model to investigate the response of the model’s seasonal simulations of summer precipitation to high-frequency variability of soil moisture. Specifically, it focuses on the response of model precipitation and temperature over the U.S. Midwest and Southeast to imposed changes in the diurnal and synoptic variability of soil moisture in 1988 and 1993. High-frequency variability of soil moisture increases (decreases) precipitation in the 1988 drought (1993 flood) year in the central and southern-tier states, except along the Gulf Coast, but causes smaller changes in precipitation along the northern-tier states. The diurnal variability and synoptic variability of soil moisture produce similar patterns of precipitation change, indicating the importance of the diurnal cycle of land surface process. The increase (decrease) in precipitation is generally accompanied by a decrease (increase) in surface and lower-tropospheric temperatures, and the changes in precipitation and temperature are attributed to both the local effect of evaporation feedback and the remote influence of large-scale water vapor transport. The precipitation increase and temperature decrease in 1988 are accompanied by an increase in local evaporation and, more importantly, by an increase in the large-scale water vapor convergence into the Midwest and Southeast. Analogous but opposite-sign behavior occurs in 1993 (compared to 1988) in changes in precipitation, temperature, soil moisture, evaporation, and large-scale water vapor transport. Results also indicate that, in regions where the model simulates the diurnal cycle of soil moisture reasonably well, including this diurnal cycle in the simulations improves model performance. However, no notable improvement in model precipitation can be found in regions where the model fails to realistically simulate the diurnal variability of soil moisture.

A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description

2005 ◽  
Vol 62 (6) ◽  
pp. 1665-1677 ◽  
Author(s):  
H. Morrison ◽  
J. A. Curry ◽  
V. I. Khvorostyanov
Keyword(s):  
High Resolution ◽  
Vertical Velocity ◽  
Large Scale ◽  
Climate Models ◽  
The Other ◽  
Microphysics Scheme ◽  
Cloud Models ◽  
Droplet Nucleation ◽  
Double Moment ◽  
Droplet Activation

Abstract A new double-moment bulk microphysics scheme predicting the number concentrations and mixing ratios of four hydrometeor species (droplets, cloud ice, rain, snow) is described. New physically based parameterizations are developed for simulating homogeneous and heterogeneous ice nucleation, droplet activation, and the spectral index (width) of the droplet size spectra. Two versions of the scheme are described: one for application in high-resolution cloud models and the other for simulating grid-scale cloudiness in larger-scale models. The versions differ in their treatment of the supersaturation field and droplet nucleation. For the high-resolution approach, droplet nucleation is calculated from Kohler theory applied to a distribution of aerosol that activates at a given supersaturation. The resolved supersaturation field and condensation/deposition rates are predicted using a semianalytic approximation to the three-phase (vapor, ice, liquid) supersaturation equation. For the large-scale version of the scheme, it is assumed that the supersaturation field is not resolved and thus droplet activation is parameterized as a function of the vertical velocity and diabatic cooling rate. The vertical velocity includes a subgrid component that is parameterized in terms of the eddy diffusivity and mixing length. Droplet condensation is calculated using a quasi-steady, saturation adjustment approach. Evaporation/deposition onto the other water species is given by nonsteady vapor diffusion allowing excess vapor density relative to ice saturation.

Global climate simulations at 2.8 km on GPU with the ICON model

2020 ◽  
Author(s):  
Xavier Lapillonne ◽  
William Sawyer ◽  
Philippe Marti ◽  
Valentin Clement ◽  
Remo Dietlicher ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Weather Prediction ◽  
Time Integration ◽  
Horizontal Resolution ◽  
Performance Comparison ◽  
Quasi Biennial Oscillation ◽  
Numerical Weather

<p>The ICON modelling framework is a unified numerical weather and climate model used for applications ranging from operational numerical weather prediction to low and high resolution climate projection. In view of further pushing the frontier of possible applications and to make use of the latest evolution in hardware technologies, parts of the model were recently adapted to run on heterogeneous GPU system. This initial GPU port focus on components required for high-resolution climate application, and allow considering multi-years simulations at 2.8 km on the Piz Daint heterogeneous supercomputer. These simulations are planned as part of the QUIBICC project “The Quasi-Biennial Oscillation (QBO) in a changing climate”, which propose to investigate effects of climate change on the dynamics of the QBO.</p><p>Because of the low compute intensity of atmospheric model the cost of data transfer between CPU and GPU at every step of the time integration would be prohibitive if only some components would be ported to the accelerator. We therefore present a full port strategy where all components required for the simulations are running on the GPU. For the dynamics, most of the physical parameterizations and infrastructure code the OpenACC compiler directives are used. For the soil parameterization, a Fortran based domain specific language (DSL) the CLAW-DSL has been considered. We discuss the challenges associated to port a large community code, about 1 million lines of code, as well as to run simulations on large-scale system at 2.8 km horizontal resolution in terms of run time and I/O constraints. We show performance comparison of the full model on CPU and GPU, achieving a speed up factor of approximately 5x, as well as scaling results on up to 2000 GPU nodes. Finally we discuss challenges and planned development regarding performance portability and high level DSL which will be used with the ICON model in the near future.</p>

scholarly journals Indian Summer Monsoon Precipitation Climatology in a High-Resolution Regional Climate Model: Impacts of Convective Parameterization on Systematic Biases

2010 ◽  
Vol 25 (2) ◽  
pp. 369-387 ◽  
Author(s):  
P. Mukhopadhyay ◽  
S. Taraphdar ◽  
B. N. Goswami ◽  
K. Krishnakumar
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Vertical Velocity ◽  
Rain Rate ◽  
Climate Model ◽  
Regional Climate ◽  
The Other ◽  
Monsoon Precipitation ◽  
Percentage Contribution ◽  
Systematic Biases

Abstract In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations.

scholarly journals Role of Soil Moisture Feedback in the Development of Extreme Summer Drought and Flood in the United States

2016 ◽  
Vol 17 (8) ◽  
pp. 2191-2207 ◽  
Author(s):  
Roop Saini ◽  
Guiling Wang ◽  
Jeremy S. Pal
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Large Scale ◽  
Climate Model ◽  
The United States ◽  
Summer Drought ◽  
Large Set ◽  
Drought And Flood ◽  
The Impact ◽  
Soil Moisture Feedback

Abstract This study tackles the contribution of soil moisture feedback to the development of extreme summer precipitation anomalies over the conterminous United States using a regional climate model. The model performs well in reproducing both the mean climate and extremes associated with drought and flood. A large set of experiments using the model are conducted that involve swapped initial soil moisture between flood and drought years using the 1988 and 2012 droughts and 1993 flood as examples. The starting time of these experiments includes 1 May (late spring) and 1 June (early summer). For all three years, the impact of 1 May soil moisture swapping is much weaker than the 1 June soil moisture swapping. In 1988 and 2012, replacing the 1 June soil moisture with that from 1993 reduces both the spatial extent and the severity of the simulated summer drought and heat. The impact is especially strong in 2012. In 1993, however, replacing the 1 June soil moisture with that from 1988 has little impact on precipitation. The contribution of soil moisture feedback to summer extremes is larger in 2012 than in 1988 and 1993. This may be because of the presence of strong anomalies in large-scale forcing in 1988 and 1993 that prohibit or favor precipitation, and the lack of such in 2012. This study demonstrates how the contribution of land–atmosphere feedback to the development of seasonal climate anomalies may vary from year to year and highlights its importance in the 2012 drought.

scholarly journals The Response of the Northwest Atlantic Ocean to Climate Change

2020 ◽  
Vol 33 (2) ◽  
pp. 405-428 ◽  
Author(s):  
Michael A. Alexander ◽  
Sang-ik Shin ◽  
James D. Scott ◽  
Enrique Curchitser ◽  
Charles Stock
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Atlantic Ocean ◽  
Gulf Stream ◽  
Large Scale ◽  
Climate Models ◽  
Gulf Of Maine ◽  
Global Climate Models ◽  
The Other ◽  
Surface Salinity

AbstractROMS, a high-resolution regional ocean model, was used to study how climate change may affect the northwestern Atlantic Ocean. A control (CTRL) simulation was conducted for the recent past (1976–2005), and simulations with additional forcing at the surface and lateral boundaries, obtained from three different global climate models (GCMs) using the RCP8.5 scenario, were conducted to represent the future (2070–99). The climate change response was obtained from the difference between the CTRL and each of the three future simulations. All three ROMS simulations indicated large increases in sea surface temperatures (SSTs) over most of the domain except off the eastern U.S. seaboard resulting from weakening of the Gulf Stream. There are also substantial intermodel differences in the response, including a southward shift of the Gulf Stream in one simulation and a slight northward shift in the other two, with corresponding changes in eddy activity. The depth of maximum warming varied among the three simulations, resulting in differences in the bottom temperature response in coastal regions, including the Gulf of Maine and the West Florida Shelf. The surface salinity decreased in the northern part of the domain and increased in the south in all three experiments, although the freshening extended much farther south in one ROMS simulation relative to the other two, and also relative to the GCM that provided the large-scale forcing. Thus, while high resolution allows for a better representation of currents and bathymetry, the response to climate change can vary considerably depending on the large-scale forcing.

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