scholarly journals Evaluation of Soil Moisture in the NCEP–NCAR and NCEP–DOE Global Reanalyses

2005 ◽  
Vol 6 (4) ◽  
pp. 391-408 ◽  
Author(s):  
Cheng-Hsuan Lu ◽  
Masao Kanamitsu ◽  
John O. Roads ◽  
Wesley Ebisuzaki ◽  
Kenneth E. Mitchell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Bank ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Moisture Analysis ◽  
Global Soil ◽  
Intercomparison Project ◽  
Atmospheric Model Intercomparison Project ◽  
Environmental Prediction ◽  
Global Reanalysis

Abstract This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

scholarly journals Operational assimilation of ASCAT surface soil wetness at the Met Office

2011 ◽  
Vol 15 (8) ◽  
pp. 2729-2746 ◽  
Author(s):  
I. Dharssi ◽  
K. J. Bovis ◽  
B. Macpherson ◽  
C. P. Jones
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Positive Impact ◽  
Surface Soil Moisture ◽  
Moisture Analysis ◽  
Analysis Scheme ◽  
Temperature And Humidity ◽  
Global Soil ◽  
Soil Wetness ◽  
Screen Temperature

Abstract. Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.

scholarly journals An Assessment of Precipitation Changes over Antarctica and the Southern Ocean since 1989 in Contemporary Global Reanalyses*

2011 ◽  
Vol 24 (16) ◽  
pp. 4189-4209 ◽  
Author(s):  
David H. Bromwich ◽  
Julien P. Nicolas ◽  
Andrew J. Monaghan
Keyword(s):  
Southern Ocean ◽  
Sea Level ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Japan Meteorological Agency ◽  
Surface Mass ◽  
Microwave Sounding ◽  
Intercomparison Project ◽  
Climate Change Assessment ◽  
Precipitation Changes

Abstract This study evaluates the temporal variability of the Antarctic surface mass balance, approximated as precipitation minus evaporation (P − E), and Southern Ocean precipitation in five global reanalyses during 1989–2009. The datasets consist of the NCEP/U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project 2 reanalysis (NCEP-2), the Japan Meteorological Agency (JMA) 25-year Reanalysis (JRA-25), ECMWF Interim Re-Analysis (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Application (MERRA), and the Climate Forecast System Reanalysis (CFSR). Reanalyses are known to be prone to spurious trends and inhomogeneities caused by changes in the observing system, especially in the data-sparse high southern latitudes. The period of study has seen a dramatic increase in the amount of satellite observations used for data assimilation. The large positive and statistically significant trends in mean Antarctic P − E and mean Southern Ocean precipitation in NCEP-2, JRA-25, and MERRA are found to be largely spurious. The origin of these artifacts varies between reanalyses. Notably, a precipitation jump in MERRA in the late 1990s coincides with the start of the assimilation of radiances from the Advanced Microwave Sounding Unit (AMSU). ERA-Interim and CFSR do not exhibit any significant trends. However, the potential impact of the assimilation of rain-affected radiances in ERA-Interim and inhomogeneities in CFSR pressure fields over Antarctica cast some doubt on the reliability of these two datasets. The authors conclude that ERA-Interim likely offers the most realistic depiction of precipitation changes in high southern latitudes during 1989–2009. The range of the trends in Antarctic P − E among the reanalyses is equivalent to 1 mm of sea level over 21 years, which highlights the improvements still needed in reanalysis simulations to better assess the contribution of Antarctica to sea level rise. Finally, this work argues for continuing cautious use of reanalysis datasets for climate change assessment.

scholarly journals Bulk Connectivity of Global SST and Land Precipitation Variations

2021 ◽  
Author(s):  
Xiaofan Li ◽  
Zeng-Zhen Hu ◽  
Bohua Huang ◽  
Cristiana Stan
Keyword(s):  
Climate Models ◽  
Atmospheric Model ◽  
Precipitation Anomaly ◽  
Connectivity Matrix ◽  
Ensemble Averaging ◽  
Seasonal Evolution ◽  
Global Land ◽  
Precipitation Variation ◽  
Intercomparison Project ◽  
Atmospheric Model Intercomparison Project

Abstract The land climate predictability at seasonal and interannual time scales is largely due to the influence of the ocean. The connections between global sea surface temperature anomaly (SSTA) and precipitation anomaly over land as a whole are assessed using observations and Atmospheric Model Intercomparison Project (AMIP) simulations for 1957-2018 in this work. With a novel bulk connectivity matrix, the regions of SSTA having the most significant connections with global land precipitation anomaly are identified and the seasonal evolution is evaluated. The similarities and differences between the observations and AMIP simulations are examined. In both observations and AMIP simulations, SSTA in the tropical central and eastern Pacific connects strongly with the global land precipitation anomaly. Compared with that in the tropical Pacific, the connections with SSTA along the equatorial Indian and Atlantic Oceans are weaker. However, the seasonal evolution of the connection shows distinguished patterns between the observations and the AMIP simulations with the strongest (weakest) connections in October (June) in the observations, in March and October (June) in a single-member of the AMIP simulation, and in February (June) in the 17-member ensemble mean of the AMIP simulations. The ensemble averaging enhances the strength of the connectivity and improves its seasonality. The results of the bulk connectivity matrix in this work can serve as a benchmark to evaluate the connection of SSTA with global land precipitation variation in climate models.

Snow Mass over North America: Observations and Results from the Second Phase of the Atmospheric Model Intercomparison Project

2005 ◽  
Vol 6 (5) ◽  
pp. 681-695 ◽  
Author(s):  
Allan Frei ◽  
Ross Brown ◽  
James A. Miller ◽  
David A. Robinson
Keyword(s):  
North America ◽  
Large Scale ◽  
Atmospheric Model ◽  
The United States ◽  
Second Phase ◽  
Model Intercomparison ◽  
Continental Scale ◽  
Intercomparison Project ◽  
Atmospheric Model Intercomparison Project ◽  
Snow Mass

Abstract Eighteen global atmospheric general circulation models (AGCMs) participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are evaluated for their ability to simulate the observed spatial and temporal variability in snow mass, or water equivalent (SWE), over North America during the AMIP-2 period (1979–95). The evaluation is based on a new gridded SWE dataset developed from objective analysis of daily snow depth observations from Canada and the United States with snow density estimated from a simple snowpack model. Most AMIP-2 models simulate the seasonal timing and the relative spatial patterns of continental-scale SWE fairly well. However, there is a tendency to overestimate the rate of ablation during spring, and significant between-model variability is found in every aspect of the simulations, and at every spatial scale analyzed. For example, on the continental scale, the peak monthly SWE integrated over the North American continent in AMIP-2 models varies between ±50% of the observed value of ∼1500 km3. The volume of water in the snowpack, and the magnitudes of model errors, are significant in comparison to major fluxes in the continental water balance. It also appears that the median result from the suite of models tends to do a better job of estimating climatological mean features than any individual model. Year-to-year variations in large-scale SWE are only weakly correlated to observed variations, indicating that sea surface temperatures (specified from observations as boundary conditions) do not drive interannual variations of SWE in these models. These results have implications for simulations of the large-scale hydrologic cycle and for climate change impact assessments.

The Role of Boundary Conditions in AMIP-2 Simulations of the NAO

2005 ◽  
Vol 18 (7) ◽  
pp. 973-981 ◽  
Author(s):  
Judah Cohen ◽  
Allan Frei ◽  
Richard D. Rosen
Keyword(s):  
Snow Cover ◽  
Atmospheric Model ◽  
Second Phase ◽  
Atmospheric Variability ◽  
Teleconnection Patterns ◽  
Nao Index ◽  
Eurasian Snow ◽  
Decadal Scale ◽  
Intercomparison Project ◽  
Atmospheric Model Intercomparison Project

Abstract The simulated North Atlantic Oscillation (NAO) teleconnection patterns and their interannual variability are evaluated from a suite of atmospheric models participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2). In general the models simulate the observed spatial pattern well, although there are important differences among models. The NAO response to interannual variations in sea surface temperature (SST) and snow-cover boundary forcings are also evaluated. The simulated NAO indices are not correlated with the observed NAO index, despite being forced with observed SSTs, indicating that SSTs are not driving NAO variability in the models. Similarly, although a number of studies have identified a link between Eurasian snow extent and the phase of the NAO, no such link is apparent in the AMIP-2 results. It appears that, within the framework of the AMIP-2 experiments, the NAO is an internal mode of atmospheric variability and that impacts of SSTs and Eurasian snow cover on the phase of the NAO are not discernable. However, these conclusions do not necessarily apply to decadal-scale and longer variability or to coupled atmosphere–ocean models.

Distributions of Tropical Precipitation Cluster Power and Their Changes under Global Warming. Part II: Long-Term Time Dependence in Coupled Model Intercomparison Project Phase 5 Models

2017 ◽  
Vol 30 (20) ◽  
pp. 8045-8059 ◽  
Author(s):  
Kevin M. Quinn ◽  
J. David Neelin
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
Rain Rate ◽  
Climate Model ◽  
Coupled Model ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Model Intercomparison ◽  
Intense Storm ◽  
Intercomparison Project

Abstract Distributions of precipitation cluster power (latent heat release rate integrated over contiguous precipitating pixels) are examined in 1°–2°-resolution members of phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate model ensemble. These approximately reproduce the power-law range and large event cutoff seen in observations and the High Resolution Atmospheric Model (HiRAM) at 0.25°–0.5° in Part I. Under the representative concentration pathway 8.5 (RCP8.5) global warming scenario, the change in the probability of the most intense storm clusters appears in all models and is consistent with HiRAM output, increasing by up to an order of magnitude relative to historical climate. For the three models in the ensemble with continuous time series of high-resolution output, there is substantial variability on when these probability increases for the most powerful storm clusters become detectable, ranging from detectable within the observational period to statistically significant trends emerging only after 2050. A similar analysis of National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) AMIP-II reanalysis and Special Sensor Microwave Imager and Imager/Sounder (SSM/I and SSMIS) rain-rate retrievals in the recent observational record does not yield reliable evidence of trends in high power cluster probabilities at this time. However, the results suggest that maintaining a consistent set of overlapping satellite instrumentation with improvements to SSM/I–SSMIS rain-rate retrieval intercalibrations would be useful for detecting trends in this important tail behavior within the next couple of decades.

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