Investigation of Precipitation Variations over Wet and Dry Areas from Observation and Model

Our observational study revealed that the precipitation increased over the wet area and decreased over the dry area during the past two decades. Here, we further investigate whether the current atmospheric models can quantitatively capture the characteristics of precipitation from the observation. The NASA Goddard Institute for Space Studies (GISS) model is used to examine the historic simulation of the precipitation, in which the historic greenhouse gases and aerosols are included in the radiative forcing. The consistency between the historic GISS simulation and the Global Precipitation Climatology Project (GPCP) precipitation suggests that the model can qualitatively capture the temporal trends of precipitation over the wet and dry areas. However, the precipitation trends are weaker in the model than in the observation. The observed trends of precipitation do not appear in the control simulation with the fixed concentrations of greenhouse gases and aerosols, which suggests that the global warming due to anthropogenic forcing can influence the temporal variations of precipitation over the wet and dry areas. Diagnostic studies of other variables from the model further suggest that enhanced rising air can increase the precipitation over the wet area.

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The Bakerian Lecture, 1991 The predictability of weather and climate

Philosophical Transactions of the Royal Society of London Series A Physical and Engineering Sciences ◽

10.1098/rsta.1991.0136 ◽

1991 ◽

Vol 337 (1648) ◽

pp. 521-572 ◽

Cited By ~ 10

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

Human Activities ◽

Radiative Forcing ◽

El Nino ◽

Numerical Models ◽

Milankovitch Cycles ◽

The Past ◽

Weather And Climate ◽

Sst Anomalies

There is large public and political interest in the predictability of weather and climate, in particular in the influence of human activities on the likely climate change during the next century. Numerical models are the main tools which enable the nonlinear processes involved in the dynamics and physics of the atmosphere and other components of the climate system to be integrated in an effective way. The performance of such models used for weather forecasting has continued to improve as more accurate data with better coverage has become available, as improved descriptions of the physics and dynamics have been incorporated and as computing capacity and speed have increased. Studies of the predictability with models suggest that with further improvements in data and models deterministic forecasting of detailed weather may ultimately have useful skill up to 2-3 weeks ahead. Beyond the limit of deterministic forecasting, some skill remains for the forecasting of general weather patterns which can be pursued by studying ensembles of model forecasts from slightly varying initial conditions. The largest difficulty with further improvements of numerical models lies in their inadequate treatment of the motions too small to be explicitly resolved. Interactions between the atmosphere and the ocean are responsible for substantial variations on seasonal, interannual and longer timescales. Forecasts are being provided of seasonal precipitation in the Sahel region of Africa based on a knowledge of global sea surface tem perature (SST) anomalies together with the assumption that such anomalies tend to persist from one season to the next. Attempts to forecast SST anomalies have centred on tropical regions in particular on the El Nino. Simple models show some skill in forecasting El Nino events 3-9 months in advance. Studies with more elaborate models which as yet only show partial success in simulating these events demonstrate the complex nature of the interactions involved. Turning to the likely changes in climate next century: if no changes occur in the atmosphere other than the increase in C0 2 and other greenhouse gases due to human activities, the increase in radiative forcing due to a doubling of atmospheric C0 2 concentration would lead to an increase of about 1.2 °C in global average temperature. Water vapour and ice-albedo feedbacks raise this to a figure of about 2.5 °C (with an uncertainty range of 1.5—4.5 °C) as estimated by the Intergovernmental Panel for Climate Change. Such a change would dominate over forcing likely to arise from other factors, and this estim ated rate of change next century is probably greater than any which has occurred on earth during the past 10000 years. The main uncertainties in climate change predictions arise from the inadequacies of the models in their descriptions of cloud-radiation and ocean circulation feedbacks. Until there is more confidence in the treatment of these feedbacks there are bound to be large uncertainties associated with any predictions of regional climate change. To reduce the uncertainties there need to be improvements in computer power, in model formulation and in our understanding of climate processes together with a large programme of observations of climate parameters to provide early detection of climate change and to provide validation of climate models and to provide data for initialization of model integrations. An important question is whether changes in climate due to changes in radiative forcing are predictable. It is pointed out that the response to climate over the past half million years to changes in forcing due to the variations in the Earth ’s orbit (Milankovitch cycles) is a regular one; some 60% of variations in the global temperature as established from the palaeontological record occur near frequencies of the Milankovitch cycles. We can, therefore, expect the changes in climate due to increasing greenhouse gases to be a largely predictable response. Large, but probably predictable, changes in the circulation of the deep ocean have modified climate change during past epochs and could have significant influence on future climate change.

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Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0168.1 ◽

2017 ◽

Vol 18 (6) ◽

pp. 1617-1641 ◽

Cited By ~ 116

Author(s):

Pingping Xie ◽

Robert Joyce ◽

Shaorong Wu ◽

Soo-Hyun Yoo ◽

Yelena Yarosh ◽

...

Keyword(s):

High Resolution ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Cold Season ◽

Superior Performance ◽

Climate Data ◽

Integration Algorithm ◽

Satellite Precipitation ◽

Precipitation Estimates ◽

Global Precipitation

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

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Closing the Global Water Vapor Budget with AIRS Water Vapor, MERRA Reanalysis, TRMM and GPCP Precipitation, and GSSTF Surface Evaporation

Journal of Climate ◽

10.1175/2011jcli4154.1 ◽

2011 ◽

Vol 24 (24) ◽

pp. 6307-6321 ◽

Cited By ~ 26

Author(s):

Sun Wong ◽

Eric J. Fetzer ◽

Brian H. Kahn ◽

Baijun Tian ◽

Bjorn H. Lambrigtsen ◽

...

Keyword(s):

Water Vapor ◽

Large Scale ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Surface Evaporation ◽

Nasa Goddard Space ◽

Intraseasonal Variations ◽

Global Precipitation ◽

Water Vapor Budget

Abstract The authors investigate if atmospheric water vapor from remote sensing retrievals obtained from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and the water vapor budget from the NASA Goddard Space Flight Center (GSFC) Modern Era Retrospective-analysis for Research and Applications (MERRA) are physically consistent with independently synthesized precipitation data from the Tropical Rainfall Measuring Mission (TRMM) or the Global Precipitation Climatology Project (GPCP) and evaporation data from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF). The atmospheric total water vapor sink (Σ) is estimated from AIRS water vapor retrievals with MERRA winds (AIRS–MERRA Σ) as well as directly from the MERRA water vapor budget (MERRA–MERRA Σ). The global geographical distributions as well as the regional wavelet amplitude spectra of Σ are then compared with those of TRMM or GPCP precipitation minus GSSTF surface evaporation (TRMM–GSSTF and GPCP–GSSTF P − E, respectively). The AIRS–MERRA and MERRA–MERRA Σs reproduce the main large-scale patterns of global P − E, including the locations and variations of the ITCZ, summertime monsoons, and midlatitude storm tracks in both hemispheres. The spectra of regional temporal variations in Σ are generally consistent with those of observed P − E, including the annual and semiannual cycles, and intraseasonal variations. Both AIRS–MERRA and MERRA–MERRA Σs have smaller amplitudes for the intraseasonal variations over the tropical oceans. The MERRA P − E has spectra similar to that of MERRA–MERRA Σ in most of the regions except in tropical Africa. The averaged TRMM–GSSTF and GPCP–GSSTF P − E over the ocean are more negative compared to the AIRS–MERRA, MERRA–MERRA Σs, and MERRA P − E.

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GPCP Pentad Precipitation Analyses: An Experimental Dataset Based on Gauge Observations and Satellite Estimates

Journal of Climate ◽

10.1175/2769.1 ◽

2003 ◽

Vol 16 (13) ◽

pp. 2197-2214 ◽

Cited By ~ 269

Author(s):

Pingping Xie ◽

John E. Janowiak ◽

Phillip A. Arkin ◽

Robert Adler ◽

Arnold Gruber ◽

...

Keyword(s):

Intraseasonal Variability ◽

Distribution Patterns ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Data Sources ◽

Frequency Components ◽

Time Basis ◽

Global Precipitation ◽

Climate Prediction Center ◽

Merging Algorithms

Abstract As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have been constructed on a 2.5° latitude–longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjusting the pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthly GPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAP is not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences in the input data sources and merging algorithms, causing problems in applications where joint use of the pentad and monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kinds of individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred from satellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so that the adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components in the pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation, are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproduced spatial distribution patterns of total precipitation and temporal variations of submonthly scales with relatively high quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful in monitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as one of its official products and is being updated on a quasi-real-time basis.

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d4PDF: large-ensemble and high-resolution climate simulations for global warming risk assessment

Progress in Earth and Planetary Science ◽

10.1186/s40645-020-00367-7 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Masayoshi Ishii ◽

Nobuhito Mori

Keyword(s):

Risk Assessment ◽

Global Warming ◽

High Resolution ◽

Climate Changes ◽

Future Climate ◽

Atmospheric Models ◽

Large Ensemble ◽

Past Climate ◽

The Past ◽

Climate Simulations

Abstract A large-ensemble climate simulation database, which is known as the database for policy decision-making for future climate changes (d4PDF), was designed for climate change risk assessments. Since the completion of the first set of climate simulations in 2015, the database has been growing continuously. It contains the results of ensemble simulations conducted over a total of thousands years respectively for past and future climates using high-resolution global (60 km horizontal mesh) and regional (20 km mesh) atmospheric models. Several sets of future climate simulations are available, in which global mean surface air temperatures are forced to be higher by 4 K, 2 K, and 1.5 K relative to preindustrial levels. Nonwarming past climate simulations are incorporated in d4PDF along with the past climate simulations. The total data volume is approximately 2 petabytes. The atmospheric models satisfactorily simulate the past climate in terms of climatology, natural variations, and extreme events such as heavy precipitation and tropical cyclones. In addition, data users can obtain statistically significant changes in mean states or weather and climate extremes of interest between the past and future climates via a simple arithmetic computation without any statistical assumptions. The database is helpful in understanding future changes in climate states and in attributing past climate events to global warming. Impact assessment studies for climate changes have concurrently been performed in various research areas such as natural hazard, hydrology, civil engineering, agriculture, health, and insurance. The database has now become essential for promoting climate and risk assessment studies and for devising climate adaptation policies. Moreover, it has helped in establishing an interdisciplinary research community on global warming across Japan.

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Temporal trends in mortality and readmission after acute heart failure: A systematic review and meta‐regression in the past four decades

European Journal of Heart Failure ◽

10.1002/ejhf.2103 ◽

2021 ◽

Author(s):

Antoine Kimmoun ◽

Koji Takagi ◽

Emmanuel Gall ◽

Shiro Ishihara ◽

Pierre Hammoum ◽

...

Keyword(s):

Heart Failure ◽

Systematic Review ◽

Acute Heart Failure ◽

Temporal Trends ◽

The Past ◽

Meta Regression

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Improved estimates of preindustrial biomass burning reduce the magnitude of aerosol climate forcing in the Southern Hemisphere

Science Advances ◽

10.1126/sciadv.abc1379 ◽

2021 ◽

Vol 7 (22) ◽

pp. eabc1379

Author(s):

Pengfei Liu ◽

Jed O. Kaplan ◽

Loretta J. Mickley ◽

Yang Li ◽

Nathan J. Chellman ◽

...

Keyword(s):

Southern Hemisphere ◽

Radiative Forcing ◽

Ice Core ◽

Ice Cores ◽

Terrestrial Ecosystems ◽

Rapid Expansion ◽

Past Century ◽

Fire Emissions ◽

The Past ◽

Fire Activity

Fire plays a pivotal role in shaping terrestrial ecosystems and the chemical composition of the atmosphere and thus influences Earth’s climate. The trend and magnitude of fire activity over the past few centuries are controversial, which hinders understanding of preindustrial to present-day aerosol radiative forcing. Here, we present evidence from records of 14 Antarctic ice cores and 1 central Andean ice core, suggesting that historical fire activity in the Southern Hemisphere (SH) exceeded present-day levels. To understand this observation, we use a global fire model to show that overall SH fire emissions could have declined by 30% over the 20th century, possibly because of the rapid expansion of land use for agriculture and animal production in middle to high latitudes. Radiative forcing calculations suggest that the decreasing trend in SH fire emissions over the past century largely compensates for the cooling effect of increasing aerosols from fossil fuel and biofuel sources.

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Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Weather and Forecasting ◽

10.1175/waf-d-16-0039.1 ◽

2016 ◽

Vol 31 (5) ◽

pp. 1409-1416 ◽

Cited By ~ 8

Author(s):

Shigenori Otsuka ◽

Shunji Kotsuki ◽

Takemasa Miyoshi

Keyword(s):

Data Assimilation ◽

Space Time ◽

Motion Vectors ◽

The Past ◽

Variational Techniques ◽

Alternative Approach ◽

Ensemble Transform Kalman Filter ◽

Global Precipitation ◽

Time Extrapolation ◽

Satellite Mapping

Abstract Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

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Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

10.5194/egusphere-egu21-7137 ◽

2021 ◽

Author(s):

Mastawesha Misganaw Engdaw ◽

Andrew Ballinger ◽

Gabriele Hegerl ◽

Andrea Steiner

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

Climate Models ◽

Temporal Trends ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Cold Temperatures

In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981&#8211;2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.Keywords: climate change, temperature, extreme events, attribution, CMIP6&#160;Acknowledgement: This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)

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