Effects of Spectral Nudging in WRF on Arctic Temperature and Precipitation Simulations

2013 ◽  
Vol 26 (12) ◽  
pp. 3985-3999 ◽  
Author(s):  
Justin M. Glisan ◽  
William J. Gutowski ◽  
John J. Cassano ◽  
Matthew E. Higgins
Keyword(s):  
Extreme Events ◽  
Large Scale ◽  
Warm Season ◽  
Cold Season ◽  
Weather Research And Forecasting ◽  
Spectral Nudging ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Arctic Temperature ◽  
Better Than

Abstract Spectral (interior) nudging is a way of constraining a model to be more consistent with observed behavior. However, such control over model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events since nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes: what is the minimum spectral nudging needed to correct biases while not limiting the simulation of extreme events? To determine this, case studies were performed using a six-member ensemble of the Pan-Arctic Weather Research and Forecasting model (WRF) with varying spectral nudging strength, using WRF’s standard nudging as a reference point. Two periods were simulated, one in a cold season (January 2007) and one in a warm season (July 2007). Precipitation and 2-m temperature were analyzed to determine how changing spectral nudging strength impacts temperature and precipitation extremes and selected percentiles. Results suggest that there is a marked lack of sensitivity to varying degrees of nudging. Moreover, given that nudging is an artificial forcing applied in the model, an outcome of this work is that nudging strength can be considerably smaller than the WRF standard strength and still produce climate simulations that are much better than using no nudging.

scholarly journals Improving pan-european hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

2011 ◽  
Vol 8 (2) ◽  
pp. 3883-3936 ◽  
Author(s):  
R. Rojas ◽  
L. Feyen ◽  
A. Dosio ◽  
D. Bavera
Keyword(s):  
Impact Assessment ◽  
Extreme Events ◽  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Time Window ◽  
Control Period ◽  
Maximum Temperature ◽  
Data Set ◽  
Climate Simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As predictand for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

scholarly journals Projected Seasonal Changes in Large-Scale Global Precipitation and Temperature Extremes Based on the CMIP5 Ensemble

2020 ◽  
Vol 33 (13) ◽  
pp. 5651-5671 ◽  
Author(s):  
Wang Zhan ◽  
Xiaogang He ◽  
Justin Sheffield ◽  
Eric F. Wood
Keyword(s):  
Extreme Events ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Extreme Temperature ◽  
Economic Losses ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
The Future ◽  
Precipitation Events

AbstractOver the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future.

Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon?

2012 ◽  
Vol 25 (23) ◽  
pp. 8212-8237 ◽  
Author(s):  
Christopher L. Castro ◽  
Hsin-I Chang ◽  
Francina Dominguez ◽  
Carlos Carrillo ◽  
Jae-Kyung Schemm ◽  
...  
Keyword(s):  
North American ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Regional Model ◽  
Precipitation Anomaly ◽  
Early Summer ◽  
North American Monsoon ◽  
The North ◽  
Temperature And Precipitation

Abstract Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982–2000) and the corresponding NCEP–NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for high-resolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scale atmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscaling will add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard.

scholarly journals Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

2011 ◽  
Vol 15 (8) ◽  
pp. 2599-2620 ◽  
Author(s):  
R. Rojas ◽  
L. Feyen ◽  
A. Dosio ◽  
D. Bavera
Keyword(s):  
Impact Assessment ◽  
Extreme Events ◽  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Time Window ◽  
Control Period ◽  
Maximum Temperature ◽  
Data Set ◽  
Climate Simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

scholarly journals Characterizing Recent Trends in U.S. Heavy Precipitation

2016 ◽  
Vol 29 (7) ◽  
pp. 2313-2332 ◽  
Author(s):  
Martin Hoerling ◽  
Jon Eischeid ◽  
Judith Perlwitz ◽  
Xiao-Wei Quan ◽  
Klaus Wolter ◽  
...  
Keyword(s):  
United States ◽  
Radiative Forcing ◽  
Daily Precipitation ◽  
Warm Season ◽  
Heavy Precipitation ◽  
Cold Season ◽  
Tropical Eastern Pacific ◽  
Climate Simulations ◽  
Daily Events ◽  
Northern United States

Abstract Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979–2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increases in precipitation associated with heavy daily events across the northern United States and decreases across the southern United States. Diagnosis of climate simulations (CCSM4 and CAM4) reveals that the evolution of observed sea surface temperatures (SSTs) was a more important factor influencing these trends than boundary condition changes linked to external radiative forcing alone. Since 1979, the latter induces widespread, but mostly weak, increases in precipitation associated with heavy daily events. The former induces a meridional pattern of northern U.S. increases and southern U.S. decreases as observed, the magnitude of which closely aligns with observed changes, especially over the south and far west. Analysis of model ensemble spread reveals that appreciable 35-yr trends in heavy daily precipitation can occur in the absence of forcing, thereby limiting detection of the weak anthropogenic influence at regional scales. Analysis of the seasonality in heavy daily precipitation trends supports physical arguments that their changes during 1979–2013 have been intimately linked to internal decadal ocean variability and less so to human-induced climate change. Most of the southern U.S. decrease has occurred during the cold season that has been dynamically driven by an atmospheric circulation reminiscent of teleconnections linked to cold tropical eastern Pacific SSTs. Most of the northeastern U.S. increase has been a warm season phenomenon, the immediate cause for which remains unresolved.

scholarly journals Impacts of using spectral nudging on regional climate model RCA4 simulations of the Arctic

2013 ◽  
Vol 6 (3) ◽  
pp. 849-859 ◽  
Author(s):  
P. Berg ◽  
R. Döscher ◽  
T. Koenigk
Keyword(s):  
Sea Ice ◽  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Coupled System ◽  
The Arctic ◽  
Spectral Nudging ◽  
The North ◽  
Temperature And Precipitation

Abstract. The performance of the Rossby Centre regional climate model RCA4 is investigated for the Arctic CORDEX (COordinated Regional climate Downscaling EXperiment) region, with an emphasis on its suitability to be coupled to a regional ocean and sea ice model. Large biases in mean sea level pressure (MSLP) are identified, with pronounced too-high pressure centred over the North Pole in summer of over 5 hPa, and too-low pressure in winter of a similar magnitude. These lead to biases in the surface winds, which will potentially lead to strong sea ice biases in a future coupled system. The large-scale circulation is believed to be the major reason for the biases, and an implementation of spectral nudging is applied to remedy the problems by constraining the large-scale components of the driving fields within the interior domain. It is found that the spectral nudging generally corrects for the MSLP and wind biases, while not significantly affecting other variables, such as surface radiative components, two-metre temperature and precipitation.

scholarly journals Dynamics of soil formation factors and their influence on agricultural technology in the Kamennaya Steppe

2020 ◽  
pp. 5-30
Author(s):  
V. A. Isaev ◽  
V. P. Belobrov ◽  
A. L. Ivanov
Keyword(s):  
Ground Water ◽  
Water Level ◽  
Warm Season ◽  
Woody Species ◽  
Cold Season ◽  
Ground Water Level ◽  
Temperature And Precipitation ◽  
Kamennaya Steppe ◽  
Average Annual Temperature

The analysis of long-term observations in the Kamennaya Steppe (over 125 years) for climatic parameters (air temperature and precipitation), ground water level, vegetation species composition revealed the main trends in their variability. Since 1969 there has been an increase in temperature and a reduction in temperature fluctuation during the year. Over the last 30 years, the difference has reached 1.90, and over the last decade it has grown by 0.40 due to the cold season. The amount of precipitation over the same 50-year period has not changed much. In total, an increase of 45 mm was observed over the decade (1999-2008). In the XXI century, there has been registered an increase in the amount of precipitation in the cold season by 12.7% and a decrease in the warm season, which creates certain prerequisites for climate continentality mitigation during the annual cycle. During the first 70 years of observations, the groundwater level in the well No. 1 was on average at the depth of 6.5 m (5.7-7.3 m). At the end of the XX century and at the beginning of the XXI century, there was marked a pronounced rise in the ground water level, the average depth was 3.8 m, which coincided with the growth of average annual temperature and an increase in total rainfall. In this period changes in the long-term regime of ground and surface soil moisture resultedin expanding the area of wetlands and hydromorphic soils on the territory of the steppe. The period of 2009-2018 is characterized by a continued increase in average annual temperatures and a decrease in precipitation, which may lead to a seasonal change in temperature and precipitation to milder and wetter winters and warmer and drier summers. Transformation of vegetation for 100 years of observations had several stages with a general trend to change the steppe grasslands to meadow-steppe, shrubs and woody species.

How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from drought to flood

2020 ◽  
Author(s):  
Yuanyuan Ma
Keyword(s):  
Extreme Events ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Model Errors ◽  
Continuous Simulation ◽  
Spectral Nudging ◽  
Sharp Turn ◽  
The Difference

<p>Sudden turn from drought to flood (STDF) is a unique representation of intra-seasonal extreme events and occurs frequently. However, it is notoriously difficult to represent in climate simulations due to the accumulation of model errors. This study uses a regional climate model (RCM) with different initialization and nudging schemes to explore effective approaches for capturing a STDF event. Results show that the conventional continuous integration with single initialization cannot reproduce the STDF event, while nudging or re-initialization can. Furthermore, spectral nudging and re-initialization outperform the conventional continuous simulation in reproducing precipitation features, but grid nudging induces the largest biases for precipitation though it has the smallest biases for other meteorological elements. Scale separation analysis shows that the large-scale features of the conventional continuous simulation drift far from the actual fields and force erroneous small-scale features, whereas the nudging and re-initialization successfully prevent the model from drifting away from the forcing fields at large-scales. The different performance for simulating precipitation among spectral nudging, re-initialization and grid nudging can be attributed to that the former two methods generate their own small-scale information via the RCM, while grid nudging over-suppresses the small-scale information while retaining the large-scale features. The difference in small-scale features affects the simulation of different moisture fluxes and convergences, as well as clouds, and then results in diverse precipitation. These results illustrate that both the consistency with large-scale features and the local variability from small-scale features are both robust factors for reproducing precipitation features during extreme events using RCMs.</p>

scholarly journals Proxy-Based Northern Hemisphere Surface Temperature Reconstructions: Sensitivity to Method, Predictor Network, Target Season, and Target Domain

2005 ◽  
Vol 18 (13) ◽  
pp. 2308-2329 ◽  
Author(s):  
S. Rutherford ◽  
M. E. Mann ◽  
T. J. Osborn ◽  
K. R. Briffa ◽  
P D. Jones ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Large Scale ◽  
Warm Season ◽  
Cold Season ◽  
Target Domain ◽  
Proxy Data ◽  
Mean Temperature ◽  
Reconstructed Temperature ◽  
Temperature Reconstructions ◽  
Methodological Considerations

Abstract Results are presented from a set of experiments designed to investigate factors that may influence proxy-based reconstructions of large-scale temperature patterns in past centuries. The factors investigated include 1) the method used to assimilate proxy data into a climate reconstruction, 2) the proxy data network used, 3) the target season, and 4) the spatial domain of the reconstruction. Estimates of hemispheric-mean temperature are formed through spatial averaging of reconstructed temperature patterns that are based on either the local calibration of proxy and instrumental data or a more elaborate multivariate climate field reconstruction approach. The experiments compare results based on the global multiproxy dataset used by Mann and coworkers, with results obtained using the extratropical Northern Hemisphere (NH) maximum latewood tree-ring density set used by Briffa and coworkers. Mean temperature reconstructions are compared for the full NH (Tropics and extratropics, land and ocean) and extratropical continents only, withvarying target seasons (cold-season half year, warm-season half year, and annual mean). The comparisons demonstrate dependence of reconstructions on seasonal, spatial, and methodological considerations, emphasizing the primary importance of the target region and seasonal window of the reconstruction. The comparisons support the generally robust nature of several previously published estimates of NH mean temperature changes in past centuries and suggest that further improvements in reconstructive skill are most likely to arise from an emphasis on the quality, rather than quantity, of available proxy data.

Variability and Predictability of Central Asia River Flows: Antecedent Winter Precipitation and Large-Scale Teleconnections

2008 ◽  
Vol 9 (6) ◽  
pp. 1334-1349 ◽  
Author(s):  
Mathew A. Barlow ◽  
Michael K. Tippett
Keyword(s):  
Climate Variability ◽  
Central Asia ◽  
Moisture Transport ◽  
Large Scale ◽  
River Flow ◽  
Regional Scale ◽  
Warm Season ◽  
Cold Season ◽  
River Flows ◽  
Syr Darya

Abstract Warm season river flows in central Asia, which play an important role in local water resources and agriculture, are shown to be closely related to the regional-scale climate variability of the preceding cold season. The peak river flows occur in the warm season (April–August) and are highly correlated with the regional patterns of precipitation, moisture transport, and jet-level winds of the preceding cold season (November–March), demonstrating the importance of regional-scale variability in determining the snowpack that eventually drives the rivers. This regional variability is, in turn, strongly linked to large-scale climate variability and tropical sea surface temperatures (SSTs), with the circulation anomalies influencing precipitation through changes in moisture transport. The leading pattern of regional climate variability, as resolved in the operationally updated NCEP–NCAR reanalysis, can be used to make a skillful seasonal forecast for individual river flow stations. This ability to make predictions based on regional-scale climate data is of particular use in this data-sparse area of the world. The river flow is considered in terms of 24 stations in Uzbekistan and Tajikistan available for 1950–85, with two additional stations available for 1958–2003. These stations encompass the headwaters of the Amu Darya and Syr Darya, two of the main rivers of central Asia and the primary feeders of the catastrophically shrinking Aral Sea. Canonical correlation analysis (CCA) is used to forecast April–August flows based on the period 1950–85; cross-validated correlations exceed 0.5 for 10 of the stations, with a maximum of 0.71. Skill remains high even after 1985 for two stations withheld from the CCA: the correlation for 1986–2002 for the Syr Darya at Chinaz is 0.71, and the correlation for the Amu Darya at Kerki is 0.77. The forecast is also correlated to the normalized difference vegetation index (NDVI); maximum values exceed 0.8 at 8-km resolution, confirming the strong connection between hydrology and growing season vegetation in the region and further validating the forecast methodology.

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