Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900

2010 ◽  
Vol 23 (21) ◽  
pp. 5755-5770 ◽  
Author(s):  
Thomas M. Smith ◽  
Phillip A. Arkin ◽  
Mathew R. P. Sapiano ◽  
Ching-Yee Chang
Keyword(s):  
Twentieth Century ◽  
Time Scales ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Phase Relationship ◽  
Coupled Model ◽  
Data Availability ◽  
Monthly Precipitation ◽  
Interannual Variations

Abstract A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented. Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric response to changing temperature and pressure, combined with interannual variations. The reconstruction makes it possible to evaluate global precipitation variations for periods much longer than the satellite period, which begins in 1979. Evaluations show some large-scale similarities with coupled model precipitation variations over the twentieth century, including an increasing tendency over the century. The reconstructed land and sea trends tend to be out of phase at low latitudes, similar to the out-of-phase relationship for interannual variations. This reconstruction may be used for climate monitoring, for statistical climate studies of the twentieth century, and for helping to evaluate dynamic climate models. In the future the possibility of improving the reconstruction will be explored by further improving the analysis methods and including additional data.

scholarly journals Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and Climate

2010 ◽  
Vol 23 (22) ◽  
pp. 5933-5957 ◽  
Author(s):  
G. M. Martin ◽  
S. F. Milton ◽  
C. A. Senior ◽  
M. E. Brooks ◽  
S. Ineson ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Full Range ◽  
Model Development ◽  
Model Performance ◽  
Systematic Errors ◽  
Wide Range

Abstract The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

scholarly journals Coupling between Large-Scale Atmospheric Processes and Mesoscale Land–Atmosphere Interactions in the U.S. Southern Great Plains during Summer. Part II: Mean Impacts of the Mesoscale

2004 ◽  
Vol 5 (6) ◽  
pp. 1247-1258 ◽  
Author(s):  
Christopher P. Weaver
Keyword(s):  
Case Studies ◽  
Time Scales ◽  
Great Plains ◽  
Large Scale ◽  
Spatial Scales ◽  
Surface Heterogeneity ◽  
Atmospheric Modeling ◽  
Mesoscale Circulations ◽  
Southern Great Plains ◽  
Atmospheric Processes

Abstract This is Part II of a two-part study of mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. Part I focused on case studies drawn from monthlong (July 1995–97), high-resolution Regional Atmospheric Modeling System (RAMS) simulations carried out to investigate these interactions. These case studies were chosen to highlight key features of the lower-tropospheric mesoscale circulations that frequently arise in this region and season due to mesoscale heterogeneity in the surface fluxes. In this paper, Part II, the RAMS-simulated mesoscale dynamical processes described in the Part I case studies are examined from a domain-averaged perspective to assess their importance in the overall regional hydrometeorology. The spatial statistics of key simulated mesoscale variables—for example, vertical velocity and the vertical flux of water vapor—are quantified here. Composite averages of the mesoscale and large-scale-mean variables over different meteorological or dynamical regimes are also calculated. The main finding is that, during dry periods, or similarly, during periods characterized by large-scale-mean subsidence, the characteristic signature of surface-heterogeneity-forced mesoscale circulations, including enhanced vertical motion variability and enhanced mesoscale fluxes in the lowest few kilometers of the atmosphere, consistently emerges. Furthermore, the impact of these mesoscale circulations is nonnegligible compared to the large-scale dynamics at domain-averaged (200 km × 200 km) spatial scales and weekly to monthly time scales. These findings support the hypothesis that the land– atmosphere interactions associated with mesoscale surface heterogeneity can provide pathways whereby diurnal, mesoscale atmospheric processes can scale up to have more general impacts at larger spatial scales and over longer time scales.

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals A Theory of the Wind-Driven Beaufort Gyre Variability

2016 ◽  
Vol 46 (11) ◽  
pp. 3263-3278 ◽  
Author(s):  
Georgy E. Manucharyan ◽  
Michael A. Spall ◽  
Andrew F. Thompson
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Climate Models ◽  
Numerical Models ◽  
Mesoscale Eddy ◽  
Eddy Diffusivity ◽  
The Arctic ◽  
Equilibration Time ◽  
Ekman Pumping ◽  
Beaufort Gyre

AbstractThe halocline of the Beaufort Gyre varies significantly on interannual to decadal time scales, affecting the freshwater content (FWC) of the Arctic Ocean. This study explores the role of eddies in the Ekman-driven gyre variability. Following the transformed Eulerian-mean paradigm, the authors develop a theory that links the FWC variability to the stability of the large-scale gyre, defined as the inverse of its equilibration time. The theory, verified with eddy-resolving numerical simulations, demonstrates that the gyre stability is explicitly controlled by the mesoscale eddy diffusivity. An accurate representation of the halocline dynamics requires the eddy diffusivity of 300 ± 200 m2 s−1, which is lower than what is used in most low-resolution climate models. In particular, on interannual and longer time scales the eddy fluxes and the Ekman pumping provide equally important contributions to the FWC variability. However, only large-scale Ekman pumping patterns can significantly alter the FWC, with spatially localized perturbations being an order of magnitude less efficient. Lastly, the authors introduce a novel FWC tendency diagnostic—the Gyre Index—that can be conveniently calculated using observations located only along the gyre boundaries. Its strong predictive capabilities, assessed in the eddy-resolving model forced by stochastic winds, suggest that the Gyre Index would be of use in interpreting FWC evolution in observations as well as in numerical models.

scholarly journals The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale

2018 ◽  
Vol 99 (11) ◽  
pp. 2341-2359 ◽  
Author(s):  
M. J. Roberts ◽  
P. L. Vidale ◽  
C. Senior ◽  
H. T. Hewitt ◽  
C. Bates ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Time Scales ◽  
Climate Models ◽  
Water Cycle ◽  
Regional Scale ◽  
Coupled Model ◽  
Convective Precipitation ◽  
Climate Simulation ◽  
Intergovernmental Panel

AbstractThe time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).

scholarly journals Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1

2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
General Circulation Models

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.

Did tropical precipitation improve in CMIP6 simulations?

2020 ◽  
Author(s):  
Stephanie Fiedler ◽  
Traute Crueger ◽  
Roberta D’Agostino ◽  
Karsten Peters ◽  
Tobias Becker ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Internal Variability ◽  
Atmospheric Composition ◽  
Tropical Precipitation ◽  
General Aspects

<p>Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Niño Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.</p><p>All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.</p>

Changes in the Past and Projected Western North Pacific Tropical Cyclone Activity in a Warmed Climate

2020 ◽  
Author(s):  
Liang Wu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
Climate Models ◽  
Rapid Development ◽  
Current Trend ◽  
Coupled Model ◽  
Rcp 8.5 ◽  
Projected Changes

<p><span>Two high-resolution climate models (the HiRAM and MRI-AGCM3.2) are used to simulate present-day western North Pacific (WNP) tropical cyclone (TC) activity and investigate </span><span>the </span><span>projected changes for the late 21<sup>st</sup> century. Compared </span><span>to</span><span>observation</span><span>s</span><span>, the models </span><span>are</span><span> able to realistically simulate many basic features of </span><span>the WNP</span><span> TC activity </span><span>climatolog</span><span>y. Future projections </span><span>with the coupled model inter-comparison project phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 8.5 scenario</span><span> show a tendency for decreases in the number of WNP TCs</span><span>,</span> <span>and of</span><span> increase</span><span>s</span> <span>in the</span> <span>more intense </span><span>TCs. It is unknown to what cause this inverse variation with number and intensity should be generally linked to similar large-scale environmental conditions. To examine the WNP TC genesis and intensity with environmental variables, we show that most of the current trend of decreasing genesis of TCs can be attributed to weakened dynamic environments and the current trend of increasing intensity of TCs might be linked to increased thermodynamic environments. Thus, the future climate warms under RCP 8.5 will likely lead to strong reductions in TC genesis frequency over the WNP, with project decreases of 36-63% by the end of the twenty-first century, but lead to greater TC intensities with rapid development of thermodynamic environments.</span></p>

scholarly journals Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales

2016 ◽  
Vol 29 (21) ◽  
pp. 7773-7795 ◽  
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
George N. Kiladis ◽  
Kevin E. Trenberth
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Global Climate ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Random Errors ◽  
Climate Data ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Daily Time

Abstract Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.

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