New Metrics to Quantify Spatial and Temporal Characteristics of Precipitation Using 20-years TRMM-GPM Data for Evaluating Climate Models

2020 ◽  
Author(s):  
Shuyi Chen ◽  
Brandon Kerns
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Satellite Observation ◽  
Warm Pool ◽  
Spatial Filter ◽  
Early Results ◽  
Wide Range ◽  
Need To Evaluate ◽  
Global And Local ◽  
Global Precipitation

<p>Precipitation is a highly complex, multiscale entity in the global weather and climate system. It is affected by both global and local circulations over a wide range of time scales from hours to weeks and beyond. It is also an important measure of the water and energy cycle in climate models. To better understand the physical processes controlling precipitation in climate models, we need to evaluate precipitation not only in in terms of its global climatological distribution but also multiscale variability in time and space.</p><p>This study presents a new set of metrics to quantify characteristics of global precipitation using 20-years the TRMM-GPM Multisatellite Precipitation Analysis (TMPA) data from June 1998 to May 2018 over the global tropics-midlatitudes (50°S – 50°N) with 3-hourly and 0.25-degree resolutions.  We developed a method to identify large-scale precipitation objects (LPOs) using a temporal-spatial filter and then track the LPOs in time, namely the Large-scale Precipitation Tracking systems (LPTs) as described in Kerns and Chen (2016, 2020, JGR-Atmos). The most unique feature of this method is that it can distinguish large-scale precipitation organized by, for example, monsoons and the Madden-Julian Oscillation (MJO), from that of mesoscale and synoptic scale weather systems, as well as those relatively stationary local topographically and diurnally forced precipitation. The new precipitation metrics based on the satellite observation are used to evaluate climate models.  Early results show that most models overproduce precipitation over land in non-LPTs and underestimate large-scale precipitation (LPTs) over the oceans compared with the observations. For example, the MJO contributes up to 40-50% of the observed annual precipitation over the Indio-Pacific warm pool region, which are usually much less in the models because of models’ inability to represent the MJO dynamics. Furthermore, the spatial variability of precipitation associated with ENSO is more pronounced in the observations than models.</p>

scholarly journals Economics of the disintegration of the Greenland ice sheet

2019 ◽  
Vol 116 (25) ◽  
pp. 12261-12269 ◽  
Author(s):  
William Nordhaus
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Social Cost ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Reduced Form ◽  
Small Contribution ◽  
Wide Range ◽  
The Impact

Concerns about the impact on large-scale earth systems have taken center stage in the scientific and economic analysis of climate change. The present study analyzes the economic impact of a potential disintegration of the Greenland ice sheet (GIS). The study introduces an approach that combines long-run economic growth models, climate models, and reduced-form GIS models. The study demonstrates that social cost–benefit analysis and damage-limiting strategies can be usefully extended to illuminate issues with major long-term consequences, as well as concerns such as potential tipping points, irreversibility, and hysteresis. A key finding is that, under a wide range of assumptions, the risk of GIS disintegration makes a small contribution to the optimal stringency of current policy or to the overall social cost of climate change. It finds that the cost of GIS disintegration adds less than 5% to the social cost of carbon (SCC) under alternative discount rates and estimates of the GIS dynamics.

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Examining the respective roles of greenhouse-gas and aerosol forcing for modes of multi-decadal variability

2020 ◽  
Author(s):  
Andrea Dittus ◽  
Ed Hawkins ◽  
Laura Wilcox ◽  
Dan Hodson ◽  
Jon Robson ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Large Scale ◽  
Climate Model ◽  
Decadal Variability ◽  
Climate Projections ◽  
Anthropogenic Aerosol ◽  
Multidecadal Variability ◽  
Aerosol Forcing ◽  
Early Results ◽  
Wide Range

<p>The respective roles of aerosol and greenhouse-gas forcing in modulating the phasing and amplitude of large-scale modes of multi-decadal variability remain poorly understood, despite the attention that has been devoted to trying to separate the influence of forcing from internal variability in modes such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation, for instance. However, understanding what drives multidecadal variability in these basins is imperative for improving near-term climate projections.</p><p>Here, we show how aerosol and greenhouse-gas forcing interact with internal climate variability to generate indices of multi-decadal variability in the Atlantic, using a large ensemble of historical simulations with HadGEM3-GC3.1 for the period 1850-2014, where anthropogenic aerosol emissions are scaled to sample a wide range in historical aerosol forcing. These results are complemented by early results from new stabilised warming simulations with the same climate model and analysis of future projections from models partaking in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6).</p>

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 Evaluating the hydrological consistency of satellite based water cycle components

2016 ◽  
Author(s):  
Oliver López ◽  
Rasmus Houborg ◽  
Matthew F. McCabe
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Large Scale ◽  
Water Cycle ◽  
Satellite Observation ◽  
Global Scale ◽  
Satellite Observations ◽  
Arid Environments ◽  
Basin Scale ◽  
Wide Range

Abstract. Advances in multi-satellite based observations of the earth system have provided the capacity to retrieve information across a wide-range of land surface hydrological components and provided an opportunity to characterize terrestrial processes from a completely new perspective. Given the spatial advantage that space-based observations offer, several regional-to-global scale products have been developed, offering insights into the multi-scale behaviour and variability of hydrological states and fluxes. However, one of the key challenges in the use of satellite-based products is characterizing the degree to which they provide realistic and representative estimates of the underlying retrieval: that is, how accurate are the hydrological components derived from satellite observations? The challenge is intrinsically linked to issues of scale, since the availability of high-quality in-situ data is limited, and even where it does exist, is generally not commensurate to the resolution of the satellite observation. Basin-scale studies have shown considerable variability in achieving water budget closure with any degree of accuracy using satellite estimates of the water cycle. In order to assess the suitability of this type of approach for evaluating hydrological observations, it makes sense to first test it over environments with restricted hydrological inputs, before applying it to more hydrological complex basins. Here we explore the concept of hydrological consistency, i.e. the physical considerations that the water budget impose on the hydrologic fluxes and states to be temporally and spatially linked, to evaluate the reproduction of a set of large-scale evaporation (E) products by using a combination of satellite rainfall (P) and Gravity Recovery and Climate Experiment (GRACE) observations of storage change, focusing on arid and semi-arid environments, where the hydrological flows can be more realistically described. Our results indicate no persistent hydrological consistency in these environments, suggesting the need for continued efforts in improving satellite observations, particularly for the retrieval of evaporation, and the need to more directly account for anthropogenic influences such as agricultural irrigation into our large scale water cycle studies.

scholarly journals Parameterization of the Spatial Variability of Rain for Large-Scale Models and Remote Sensing

2015 ◽  
Vol 54 (10) ◽  
pp. 2027-2046 ◽  
Author(s):  
Z. J. Lebo ◽  
C. R. Williams ◽  
G. Feingold ◽  
V. E. Larson
Keyword(s):  
Spatial Variability ◽  
Large Scale ◽  
Rain Rate ◽  
Numerical Models ◽  
Spatial Scales ◽  
Warm Pool ◽  
Radar Data ◽  
Model Output ◽  
Wide Range ◽  
Averaged Model

AbstractThe spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

scholarly journals Major Characteristics of Southern Ocean Cloud Regimes and Their Effects on the Energy Budget

2011 ◽  
Vol 24 (19) ◽  
pp. 5061-5080 ◽  
Author(s):  
John M. Haynes ◽  
Christian Jakob ◽  
William B. Rossow ◽  
George Tselioudis ◽  
Josephine Brown
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Climate Models ◽  
Satellite Observation ◽  
Radiative Heating ◽  
Radiation Balance ◽  
Heating Rates ◽  
Cloud Systems ◽  
Low Cloud ◽  
Cloud Regimes

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79% of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

scholarly journals Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

2020 ◽  
Vol 101 (11) ◽  
pp. E1980-E1995 ◽  
Author(s):  
Stephan Rasp ◽  
Hauke Schulz ◽  
Sandrine Bony ◽  
Bjorn Stevens
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Climate Models ◽  
Training Dataset ◽  
Trade Wind ◽  
Radiation Balance ◽  
Shallow Convection ◽  
Wide Range ◽  
Shallow Clouds ◽  
New Phenomena

AbstractHumans excel at detecting interesting patterns in images, for example, those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowdsourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish, and Gravel. On cloud-labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowdsourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.

scholarly journals A Moist Static Energy Budget–Based Analysis of the Sahel Rainfall Response to Uniform Oceanic Warming

2017 ◽  
Vol 30 (15) ◽  
pp. 5637-5660 ◽  
Author(s):  
Spencer A. Hill ◽  
Yi Ming ◽  
Isaac M. Held ◽  
Ming Zhao
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Order Term ◽  
Lower Troposphere ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Moist Static Energy ◽  
Wet Season ◽  
Wide Range ◽  
Sst Warming ◽  
Static Energy

Climate models generate a wide range of precipitation responses to global warming in the African Sahel, but all that use the NOAA Geophysical Fluid Dynamics Laboratory AM2.1 model as their atmospheric component dry the region sharply. This study compares the Sahel’s wet season response to uniform 2-K SST warming in AM2.1 using either its default convective parameterization, relaxed Arakawa–Schubert (RAS), or an alternate, the University of Washington (UW) parameterization, using the moist static energy (MSE) budget to diagnose the relevant mechanisms. UW generates a drier, cooler control Sahel climate than does RAS and a modest rainfall increase with SST warming rather than a sharp decrease. Horizontal advection of dry, low-MSE air from the Sahara Desert—a leading-order term in the control MSE budget with either parameterization—is enhanced with oceanic warming, driven by enhanced meridional MSE and moisture gradients spanning the Sahel. With RAS, this occurs throughout the free troposphere and is balanced by anomalous MSE import through anomalous subsidence, which must be especially large in the midtroposphere where the moist static stability is small. With UW, the strengthening of the meridional MSE gradient is mostly confined to the lower troposphere, due in part to comparatively shallow prevailing convection. This necessitates less subsidence, enabling convective and total precipitation to increase with UW, although both large-scale precipitation and precipitation minus evaporation decrease. This broad set of hydrological and energetic responses persists in simulations with SSTs varied over a wide range.

The structural violence of globalization

2016 ◽  
Vol 12 (3) ◽  
pp. 222-258 ◽  
Author(s):  
Jessica Srikantia
Keyword(s):  
Multinational Corporations ◽  
Poverty Reduction ◽  
Large Scale ◽  
Structural Violence ◽  
Global System ◽  
Content Type ◽  
Operating Rules ◽  
Wide Range ◽  
Global And Local ◽  
Practical Implications

Purpose This paper aims to expose the violence intrinsic to globalization and to suggest a conceptual and practical domain focused on arresting and preventing the structural violence of globalization. Design/methodology/approach The paper integrates theory, scholarly literature and the author’s fieldwork analyzed through solidarity and liberationist methodologies. Findings The paper shows that severe, violent and irreparable destruction of formerly thriving and sustainable cultures and communities around the globe is an inherent component of globalization; current notions of “development” and “poverty” provide ideological cover for such destruction; a wide range of mainstream institutions and organizations (including governments, trade and financial institutions and national and multinational corporations) benefit from the destruction and collude in these dynamics, while a passive majority participates through its silence and consumptive lifestyle; and to arrest these dynamics requires awareness of the structural violence of development and globalization, and that those of us living in currently unsustainable societies commit both to re-localize our effects to our own communities and to change the operating rules of the global system. Practical implications This paper offers analysis, perspectives and practical considerations toward transformations essential to ending the structural violence of globalization, while inviting broad-based solidarity for further advancements. Originality/value Bridging global and local realities, the paper exposes systematic large-scale structural violence endemic to globalization, “development”, mainstream ideas about poverty and practices of “poverty reduction”. The paper identifies some fundamental requirements for arresting the structural violence of the global system.

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