scholarly journals Length and time scales of atmospheric moisture recycling

2010 ◽  
Vol 10 (9) ◽  
pp. 21867-21893 ◽  
Author(s):  
R. J. van der Ent ◽  
H. H. G. Savenije
Keyword(s):  
Time Scales ◽  
Reanalysis Data ◽  
Length Scale ◽  
Atmospheric Moisture ◽  
Climatic Effects ◽  
Study Region ◽  
Moisture Recycling ◽  
Shape Dependence ◽  
Novel Approach ◽  
The Tropics

Abstract. It is difficult to quantify the degree to which terrestrial evaporation supports the occurrence of precipitation within a certain study region (i.e. regional moisture recycling) due to the scale- and shape-dependence of regional moisture recycling ratios. In this paper we present a novel approach to quantify the spatial and temporal scale of moisture recycling, independent of the size and shape of the region under study. The calculation is based on ERA-Interim reanalysis data for the period 1999 to 2008. It is shown that in the tropics or in mountainous terrain the length scale of recycling can be as low as 500 to 2000 km. In temperate climates the length scale is typically between 3000 to 5000 km whereas it amounts to more than 7000 km in desert areas. The time scale of recycling ranges from 3 to 20 days, with the exception of deserts, where it is much higher. The most distinct seasonal differences can be observed over the Northern Hemisphere: in winter, moisture recycling is insignificant, whereas in summer it plays a major role in the climate. The length and time scales of atmospheric moisture recycling can be useful metrics to quantify local climatic effects of land use change.

scholarly journals Length and time scales of atmospheric moisture recycling

2011 ◽  
Vol 11 (5) ◽  
pp. 1853-1863 ◽  
Author(s):  
R. J. van der Ent ◽  
H. H. G. Savenije
Keyword(s):  
Time Scales ◽  
Scaling Laws ◽  
Reanalysis Data ◽  
Length Scale ◽  
Atmospheric Moisture ◽  
Climatic Effects ◽  
Study Region ◽  
Moisture Recycling ◽  
Shape Dependence ◽  
Novel Approach

Abstract. It is difficult to quantify the degree to which terrestrial evaporation supports the occurrence of precipitation within a certain study region (i.e. regional moisture recycling) due to the scale- and shape-dependence of regional moisture recycling ratios. In this paper we present a novel approach to quantify the spatial and temporal scale of moisture recycling, independent of the size and shape of the region under study. In contrast to previous studies, which essentially used curve fitting, the scaling laws presented by us follow directly from the process equation. thus allowing a fair comparison between regions and seasons. The calculation is based on ERA-Interim reanalysis data for the period 1999 to 2008. It is shown that in the tropics or in mountainous terrain the length scale of recycling can be as low as 500 to 2000 km. In temperate climates the length scale is typically between 3000 to 5000 km whereas it amounts to more than 7000 km in desert areas. The time scale of recycling ranges from 3 to 20 days, with the exception of deserts, where it is much longer. The most distinct seasonal differences can be observed over the Northern Hemisphere: in winter, moisture recycling is insignificant, whereas in summer it plays a major role in the climate. The length and time scales of atmospheric moisture recycling can be useful metrics to quantify local climatic effects of land use change.

scholarly journals High-resolution global atmospheric moisture connections from evaporation to precipitation

2020 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Jolanda J. E. Theeuwen ◽  
Arie Staal
Keyword(s):  
Amazon Basin ◽  
Reanalysis Data ◽  
Congo Basin ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
Wind Speeds ◽  
Complete Dataset ◽  
Global Land ◽  
Land Recycling ◽  
Tracking Model

Abstract. A key Earth system process is the circulation of evaporated moisture through the atmosphere. Spatial connections between evaporation and precipitation affect the global and regional climates by redistributing water and latent heat. Through this atmospheric moisture recycling, land-cover changes influence regional precipitation patterns, with potentially far-reaching effects on human livelihoods and biome distributions across the globe. However, a globally complete dataset of atmospheric moisture flows from evaporation to precipitation has been lacking so far. Here we present a dataset of global atmospheric moisture recycling on both 0.5° and 1.0° spatial resolution. We simulated the moisture flows between each pair of cells across all land and oceans for 2008–2017 and present their monthly climatological means. We applied the Lagrangian moisture tracking model UTrack, which is forced with ERA5 reanalysis data on 25 atmospheric layers and hourly wind speeds and directions. Due to the global coverage of the simulations, a complete picture of both the upwind source areas of precipitation and downwind target areas of evaporation can be obtained. We show a number of statistics of global atmospheric moisture flows: land recycling, basin recycling, mean latitudinal and longitudinal flows, absolute latitudinal and longitudinal flows, and basin recycling for the 26 largest river basins. We find that, on average, 70 % of global land evaporation rains down over land, varying between 62 % and 74 % across the year; 51 % of global land precipitation has evaporated from land, varying between 36 % and 57 % across the year. Highest basin recycling occurs in the Amazon and Congo basins, with evaporation and precipitation recycling of 63 % and 36 % for the Amazon basin and 60 % and 47 % for the Congo basin. These statistics are examples of the potential usage of the dataset, which allows users to identify and quantify the moisture flows from and to any area on Earth, from local to global scales. The dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.912710 (Tuinenburg et al., 2020).

scholarly journals Tracking the global flows of atmospheric moisture

2019 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Reanalysis Dataset ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

Abstract. Many processes in hydrology and Earth system science relate to moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture tracking models are in use that are forced with output from atmospheric models, but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends on the purpose of the study, but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis dataset has become the state-of-the-art, paving the way for a new generation of moisture tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and footprints of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model publicly available.

scholarly journals High-resolution global atmospheric moisture connections from evaporation to precipitation

2020 ◽  
Vol 12 (4) ◽  
pp. 3177-3188
Author(s):  
Obbe A. Tuinenburg ◽  
Jolanda J. E. Theeuwen ◽  
Arie Staal
Keyword(s):  
Amazon Basin ◽  
Reanalysis Data ◽  
Congo Basin ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
Wind Speeds ◽  
Complete Dataset ◽  
Global Land ◽  
Land Recycling ◽  
Tracking Model

Abstract. A key Earth system process is the circulation of evaporated moisture through the atmosphere. Spatial connections between evaporation and precipitation affect the global and regional climates by redistributing water and latent heat. Through this atmospheric moisture recycling, land cover changes influence regional precipitation patterns, with potentially far-reaching effects on human livelihoods and biome distributions across the globe. However, a globally complete dataset of atmospheric moisture flows from evaporation to precipitation has been lacking so far. Here we present a dataset of global atmospheric moisture recycling on both 0.5∘ and 1.0∘ spatial resolution. We simulated the moisture flows between each pair of cells across all land and oceans for 2008–2017 and present their monthly climatological means. We applied the Lagrangian moisture tracking model UTrack, which is forced with ERA5 reanalysis data on 25 atmospheric layers and hourly wind speeds and directions. Due to the global coverage of the simulations, a complete picture of both the upwind source areas of precipitation and downwind target areas of evaporation can be obtained. We show a number of statistics of global atmospheric moisture flows: land recycling, basin recycling, mean latitudinal and longitudinal flows, absolute latitudinal and longitudinal flows, and basin recycling for the 26 largest river basins. We find that, on average, 70 % of global land evaporation rains down over land, varying between 62 % and 74 % across the year; 51 % of global land precipitation has evaporated from land, varying between 36 % and 57 % across the year. The highest basin recycling occurs in the Amazon and Congo basins, with evaporation and precipitation recycling of 63 % and 36 % for the Amazon basin and 60 % and 47 % for the Congo basin. These statistics are examples of the potential usage of the dataset, which allows users to identify and quantify the moisture flows from and to any area on Earth, from local to global scales. The dataset is available at https://doi.org/10.1594/PANGAEA.912710 (Tuinenburg et al., 2020).

Tracking the global flows of atmospheric moisture

2020 ◽  
Author(s):  
Obbe Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Reanalysis Dataset ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

<p>Many processes in hydrology and Earth system science relate to moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture tracking models are in use that are forced with output from atmospheric models, but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends on the purpose of the study, but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis dataset has become the state-of-the-art, paving the way for a new generation of moisture tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and <q>footprints</q> of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model publicly available.</p>

scholarly journals Tracking the global flows of atmospheric moisture and associated uncertainties

2020 ◽  
Vol 24 (5) ◽  
pp. 2419-2435 ◽  
Author(s):  
Obbe A. Tuinenburg ◽  
Arie Staal
Keyword(s):  
Vertical Mixing ◽  
Three Dimensional ◽  
Reanalysis Data ◽  
Point Sources ◽  
Atmospheric Moisture ◽  
Data Set ◽  
Considerable Uncertainty ◽  
Moisture Recycling ◽  
Trade Offs ◽  
Spatial Dimensions

Abstract. Many processes in hydrology and Earth system science relate to continental moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture-tracking models are in use that are forced with output from atmospheric models but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends not only on the purpose of the study but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis data set has become the state of the art, paving the way for a new generation of moisture-tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture-tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and “footprints” of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model, which we call UTrack-atmospheric-moisture, publicly available.

scholarly journals On the "well-mixed" assumption and numerical 2-D tracing of atmospheric moisture

2013 ◽  
Vol 13 (11) ◽  
pp. 5567-5585 ◽  
Author(s):  
H. F. Goessling ◽  
C. H. Reick
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Significant Degree ◽  
Reanalysis Data ◽  
Atmospheric Moisture ◽  
Western Sahel ◽  
Atmospheric Data ◽  
Atmospheric General Circulation Models ◽  
Source Sink ◽  
The Tropics

Abstract. Atmospheric water vapour tracers (WVTs) are an elegant tool to determine source–sink relations of moisture "online" in atmospheric general circulation models (AGCMs). However, it is sometimes desirable to establish such relations "offline" based on already existing atmospheric data (e.g. reanalysis data). One simple and frequently applied offline method is 2-D moisture tracing. It makes use of the "well-mixed" assumption, which allows for treating the vertical dimension integratively. Here we scrutinise the "well-mixed" assumption and 2-D moisture tracing by means of analytical considerations in combination with AGCM-WVT simulations. We find that vertically well-mixed conditions are seldom met. Due to the presence of vertical inhomogeneities, 2-D moisture tracing (i) neglects a significant degree of fast-recycling, and (ii) results in erroneous advection where the direction of the horizontal winds varies vertically. The latter is not so much the case in the extratropics, but in the tropics this can lead to large errors. For example, computed by 2-D moisture tracing, the fraction of precipitation in the western Sahel that originates from beyond the Sahara is ~40%, whereas the fraction that originates from the tropical and Southern Atlantic is only ~4%. According to full (i.e. 3-D) moisture tracing, however, both regions contribute roughly equally, showing that the errors introduced by the 2-D approximation can be substantial.

Multiscale Convective Organization and the YOTC Virtual Global Field Campaign

2012 ◽  
Vol 93 (8) ◽  
pp. 1171-1187 ◽  
Author(s):  
Mitchell W. Moncrieff ◽  
Duane E. Waliser ◽  
Martin J. Miller ◽  
Melvyn A. Shapiro ◽  
Ghassem R. Asrar ◽  
...  
Keyword(s):  
Time Scales ◽  
Climate Models ◽  
Water Cycle ◽  
Building Blocks ◽  
Tropical Convection ◽  
Global Water Cycle ◽  
Weather And Climate ◽  
Impact Phenomena ◽  
Meteorological Systems ◽  
The Tropics

The Year of Tropical Convection (YOTC) project recognizes that major improvements are needed in how the tropics are represented in climate models. Tropical convection is organized into multiscale precipitation systems with an underlying chaotic order. These organized systems act as building blocks for meteorological events at the intersection of weather and climate (time scales up to seasonal). These events affect a large percentage of the world's population. Much of the uncertainty associated with weather and climate derives from incomplete understanding of how meteorological systems on the mesoscale (~1–100 km), synoptic scale (~1,000 km), and planetary scale (~10,000 km) interact with each other. This uncertainty complicates attempts to predict high-impact phenomena associated with the tropical atmosphere, such as tropical cyclones, the Madden–Julian oscillation, convectively coupled tropical waves, and the monsoons. These and other phenomena influence the extratropics by migrating out of the tropics and by the remote effects of planetary waves, including those generated by the MJO. The diurnal and seasonal cycles modulate all of the above. It will be impossible to accurately predict climate on regional scales or to comprehend the variability of the global water cycle in a warmer world without comprehensively addressing tropical convection and its interactions across space and time scales.

scholarly journals A Tropospheric Emission Spectrometer HDO/H<sub>2</sub>O retrieval simulator for climate models

2012 ◽  
Vol 12 (6) ◽  
pp. 13827-13880
Author(s):  
R. D. Field ◽  
C. Risi ◽  
G. A. Schmidt ◽  
J. Worden ◽  
A. Voulgarakis ◽  
...  
Keyword(s):  
Time Scales ◽  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Satellite Measurement ◽  
Climate Simulations ◽  
Accurate Comparison ◽  
Free Running ◽  
The Tropics ◽  
Short Time

Abstract. Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the modeled meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, "categorical" approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and modeled meteorology at short time scales. To test this approach, nudged simulations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the δD fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30‰ over the ocean and decreases of up to 40‰ over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had δD biases of −8‰ over ocean and −34‰ over land compared to TES δD, which were less than the biases using raw modeled δD fields.

A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

&lt;p&gt;Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 &amp;#8211; 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). &lt;span&gt;For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.&lt;/span&gt;&lt;/p&gt;

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