Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

10.5194/essd-2020-262 ◽

2020 ◽

Author(s):

Zhen Zhang ◽

Etienne Fluet-Chouinard ◽

Katherine Jensen ◽

Kyle McDonald ◽

Gustaf Hugelius ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Southern Oscillation ◽

Microwave Remote Sensing ◽

Global Scale ◽

Annual Maximum ◽

Time Step ◽

Wetland Area ◽

Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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Cities Exacerbate Climate Warming

Urban Science ◽

10.3390/urbansci5010027 ◽

2021 ◽

Vol 5 (1) ◽

pp. 27

Author(s):

Lahouari Bounoua ◽

Kurtis Thome ◽

Joseph Nigro

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Climate Models ◽

Warming Trend ◽

Climate Mitigation ◽

Temperature Changes ◽

Surface Warming ◽

The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

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Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

Scientific Reports ◽

10.1038/s41598-021-81143-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Niloufar Nouri ◽

Naresh Devineni ◽

Valerie Were ◽

Reza Khanbilvardi

Keyword(s):

Population Density ◽

Large Scale ◽

Southern Oscillation ◽

The United States ◽

Anthropogenic Factors ◽

Climate Variables ◽

Term Trend ◽

Cyclical Variability ◽

Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

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Oceanography of the New Zealand subantarctic region

10.26686/wgtn.17009291 ◽

2021 ◽

Author(s):

◽

Aitana Forcén-Vázquez

Keyword(s):

New Zealand ◽

Interannual Variability ◽

Water Column ◽

Large Scale ◽

Southern Oscillation ◽

Complex Structure ◽

Positive Trend ◽

Intermediate Water ◽

Subantarctic Region

Subantarctic New Zealand is an oceanographycally dynamic region with the Subtropical Front (STF) to the north and the Subantarctic Front (SAF) to the south. This thesis investigates the ocean structure of the Campbell Plateau and the surrounding New Zealand subantarctic, including the spatial, seasonal, interannual and longer term variability over the ocean properties, and their connection to atmospheric variability using a combination of in-situ oceanographic measurements and remote sensing data. The spatial and seasonal oceanographic structure in the New Zealand subantarctic region was investigated by analysing ten high resolution Conductivity Temperature and Depth (CTD) datasets, sampled during oceanographic cruises from May 1998 to February 2013. Position of fronts, water mass structure and changes over the seasons show a complex structure around the Campbell Plateau combining the influence of subtropical and subantarctic waters. The spatial and interannual variability on the Campbell Plateau was described by analysing approximately 70 low resolution CTD profiles collected each year in December between 2002 and 2009. Conservative temperature and absolute salinity profiles reveal high variability in the upper 200m of the water column and a homogeneous water column from 200 to 600m depth. Temperature variability of about 0.7 °C, on occasions between consecutive years, is observed down to 900m depth. The presence of Subantarctic Mode Water (SAMW) on the Campbell Plateau is confirmed and Antarctic Intermediate Water (AAIW) reported for the first time in the deeper regions around the edges of the plateau. Long-term trends and variability over the Campbell Plateau were investigated by analysing satellite derived Sea Level Anomalies (SLA) and Sea Surface Temperature (SST) time series. Links to large scale atmospheric processes are also explored through correlation with the Southern Oscillation Index (SOI) and Southern Annular Mode (SAM). SST shows a strong seasonality and interannual variability which is linked to local winds, but no significant trend is found. The SLA over the Campbell Plateau has increased at a rate of 5.2 cm decade⁻¹ in the last two decades. The strong positive trend in SLA appears to be a combination of the response of the ocean to wind stress curl (Ekman pumping), thermal expansion and ocean mass redistribution via advection amongst others. These results suggest that the variability on the Campbell Plateau is influenced by the interaction of the STF and the SAF. The STF influence reaches the limit of the SAF over the western Campbell Plateau and the SAF influence extends all around the plateau. Results also suggest different connections between the plateau with the surrounding oceans, e.g., along the northern edge with the Bounty Trough and via the southwest edge with the SAF. A significant correlation with SOI and little correlation with SAM suggest a stronger response to tropically driven processes in the long-term variability on the Campbell Plateau. The results of this thesis provide a new definitive assessment of the circulation, water masses and variability of the Campbell Plateau on mean, annual, and interannual time scales which will support research in other disciplines such as palaeoceanography, fisheries management and climate.

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General Characteristics and Variability of Climate in the Amazon Basin and its Links to the Global Climate System

The Biogeochemistry of the Amazon Basin ◽

10.1093/oso/9780195114317.003.0005 ◽

2001 ◽

Author(s):

Jose A. Marengo ◽

Carlos A. Nobre

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Amazon Basin ◽

Global Climate ◽

River System ◽

Climate System ◽

Wet Season ◽

Tropospheric Circulation ◽

The Tropics

The Amazon region is of particular interest because it represents a large source of heat in the tropics and has been shown to have a significant impact on extratropical circulation, and it is Earth’s largest and most intense land-based convective center. During the Southern Hemisphere summer when convection is best developed, the Amazon basin is one of the wettest regions on Earth. Amazonia is of course not isolated from the rest of the world, and a global perspective is needed to understand the nature and causes of climatological anomalies in Amazonia and how they feed back to influence the global climate system. The Amazon River system is the single, largest source of freshwater on Earth. The flow regime of this river system is relatively unimpacted by humans (Vörösmarty et al. 1997 a, b) and is subject to interannual variability in tropical precipitation that ultimately is translated into large variations in downstream hydrographs (Marengo et al. 1998a, Vörösmarty et al. 1996, Richey et al. 1989a, b). The recycling of local evaporation and precipitation by the forest accounts for a sizable portion of the regional water budget (Nobre et al. 1991, Eltahir 1996), and as large areas of the basin are subject to active deforestation there is grave concern about how such land surface disruptions may affect the water cycle in the tropics (see reviews in Lean et al. 1996). Previous studies have emphasized either how large-scale atmospheric circulation or land surface conditions can directly control the seasonal changes in rainfall producing mechanisms. Studies invoking controls of convection and rainfall by large-scale circulation emphasize the relationship between the establishment of upper-tropospheric circulation over Bolivia and moisture transport from the Atlantic ocean for initiation of the wet season and its intensity (see reviews in Marengo et al. 1999). On the other hand, Eltahir and Pal (1996) have shown that Amazon convection is closely related to land surface humidity and temperature, while Fu et al. (1999) indicate that the wet season in the Amazon basin is controlled by both changes in land surface temperature and the sea surface temperature (SST) in the adjacent oceans, depending if the region is north-equatorial or southern Amazonia.

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Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

Hydrology and Earth System Sciences ◽

10.5194/hess-15-533-2011 ◽

2011 ◽

Vol 15 (2) ◽

pp. 533-546 ◽

Cited By ~ 35

Author(s):

M. Becker ◽

B. Meyssignac ◽

L. Xavier ◽

A. Cazenave ◽

R. Alkama ◽

...

Keyword(s):

Large Scale ◽

Amazon Basin ◽

Hydrological Modeling ◽

Water Cycle ◽

Southern Oscillation ◽

Water Storage ◽

Terrestrial Water Storage ◽

Direct Estimate ◽

Terrestrial Water

Abstract. Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-D TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations.

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Long-Term Climate and Derived Surface Hydrology and Energy Flux Data for Mexico: 1925–2004

Journal of Climate ◽

10.1175/jcli4086.1 ◽

2007 ◽

Vol 20 (9) ◽

pp. 1936-1946 ◽

Cited By ~ 70

Author(s):

Chunmei Zhu ◽

Dennis P. Lettenmaier

Keyword(s):

Soil Moisture ◽

North American ◽

Land Surface ◽

Surface Fluxes ◽

The Other ◽

Shortwave Radiation ◽

Infiltration Capacity ◽

Time Step ◽

Downward Longwave Radiation

Abstract Studying the role of land surface conditions in the Mexican portion of the North American monsoon system (NAMS) region has been a challenge due to the paucity of long-term observations. A long-term gridded observation-based climate dataset suitable for forcing land surface models, as well as model-derived land surface states and fluxes for a domain consisting of all of Mexico, is described. The datasets span the period of January 1925–October 2004 at 1/8° spatial resolution at a subdaily (3 h) time step. The simulated runoff matches the observations plausibly over most of the 14 small river basins spanning all of Mexico, which suggests that long-term mean evapotranspiration is realistically reproduced. On this basis, and given the physically based model parameterizations of soil moisture and energy fluxes, the other surface fluxes and state variables such as soil moisture should be represented reasonably. In addition, a comparison of the surface fluxes from this study is performed with North American Regional Reanalysis (NARR) data on a seasonal mean basis. The results indicate that downward shortwave radiation is generally smaller than in the NARR data, especially in summer. Net radiation, on the other hand, is somewhat larger in the Variable Infiltration Capacity (VIC) hydrological model than in the NARR data for much of the year over much of the domain. The differences in radiative and turbulent fluxes are attributed to (i) the parameterization used in the VIC forcings for solar and downward longwave radiation, which links them to the daily temperature and temperature range, and (ii) differences in the land surface parameterizations used in VIC and the NCEP–Oregon State University–U.S. Air Force–NWS/Hydrologic Research Lab (Noah) land scheme used in NARR.

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Evaluating model-simulated and satellite-derived SM using in situ observations under different environment conditions in China

10.5194/egusphere-egu2020-2819 ◽

2020 ◽

Author(s):

Yuanyuan Wang ◽

Guicai Li

Keyword(s):

Land Surface ◽

Large Scale ◽

Microwave Remote Sensing ◽

Ground Truth ◽

Original Data ◽

Observation Data ◽

Monitoring Networks ◽

The Future ◽

In Situ Observations

Soil moisture (SM) is a key variable in understanding the climate system through its controls on the land surface energy and water budget. Large scale SM products have become increasingly available thanks to development in microwave remote sensing and land surface modeling. Comprehensive assessments on the reliability of satellite-derived and model-simulated SM products are essential for their improvement and application. In this research, the active, passive and combined Climate Change Initiative (CCI V04.2) SM products and the China Land Data Assimilation System (CLDAS V2.0) SM products were evaluated by comparing with in situ observed data over three networks in China: Hebi, Naqu and Heihe. The three sites have different environmental conditions and sensor densities, providing observations covering more than 2 years. Four statistic scores were calculated: R (considering both original data and anomalies), Bias, RMSE, ubRMSE. TC (Triple Collocation) analysis was also carried out in which uncertainties in observations are taken into account. Results indicate that the performance of the two SM products varies between the monitoring networks. For Naqu site, both products show good performance, with CCI-SM showing slightly higher R and lower ubRMSE. For Hebi site, CLDAS-SM performs better than CCI-SM, whereas for Heihe site, CLDAS-SM performs worse than CCI-SM. The expected uncertainty (0.04 m3/m3) can be achieved in Naqu and Heihe site by CCI-SM, and in Hebi and Naqu site by CLDAS-SM, which is quite encouraging. The two products agree in terms of sign of the Bias value, which is positive in Hebi and negative in Naqu and Heihe. Among all the three networks, Heihe site exhibits the lowest accuracy due to its complicated terrain and heterogeneous land surface. Ranom of CLDAS-SM in Heihe is close to 0, indicating its inability to capture short term variability. TC results reveal that for Naqu site the observation data have quite good qualities, while for Hebi site CLDAS-SM is more approximate to &#8216;ground truth&#8217; than in situ observations, suggesting a refinement of network maybe needed in the future. Overall, the two products are complementary. CLDAS-SM performs better in populated area (e.g., Hebi) where meteorological forcing is more accurate and CCI-SM performs better in remote areas (Naqu, Heihe) where RFI is usually low. More reliable validation networks are needed in the future to comprehensively understand the advantages and disadvantage of the two SM products in China.

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Challenges and Opportunities in Coastal Shoreline Prediction

Frontiers in Marine Science ◽

10.3389/fmars.2021.788657 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kristen D. Splinter ◽

Giovanni Coco

Keyword(s):

Large Scale ◽

Southern Oscillation ◽

Shoreline Change ◽

Sandy Beaches ◽

Water Levels ◽

Current State ◽

Challenges And Opportunities ◽

Future Challenges ◽

Shoreline Prediction

Sandy beaches comprise approximately 31% of the world's ice-free coasts. Sandy coastlines around the world are continuously adjusting in response to changing waves and water levels at both short (storm) and long (climate-driven, from El-Nino Southern Oscillation to sea level rise) timescales. Managing this critical zone requires robust, advanced tools that represent our best understanding of how to abstract and integrate coastal processes. However, this has been hindered by (1) a lack of long-term, large-scale coastal monitoring of sandy beaches and (2) a robust understanding of the key physical processes that drive shoreline change over multiple timescales. This perspectives article aims to summarize the current state of shoreline modeling at the sub-century timescale and provides an outlook on future challenges and opportunities ahead.

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Long-term studies of MLT summer length definitions based on mean zonal wind features observed for more than one solar cycle at mid- and high-latitudes in the northern hemisphere

10.5194/angeo-2021-51 ◽

2021 ◽

Author(s):

Juliana Jaen ◽

Toralf Renkwitz ◽

Jorge L. Chau ◽

Maosheng He ◽

Peter Hoffmann ◽

...

Keyword(s):

Solar Cycle ◽

Zonal Wind ◽

Large Scale ◽

Southern Oscillation ◽

Lower Thermosphere ◽

Polar Vortex ◽

Mean Value ◽

High Latitudes ◽

The Mean

Abstract. Specular meteor radars (SMRs) and partial reflection radars (PRRs) have been observing mesospheric winds for more than a solar cycle over Germany (~54 °N) and northern Norway (~69 °N). This work investigates the mesospheric mean zonal wind and the zonal mean geostrophic zonal wind from the Microwave Limb Sounder (MLS) over these two regions between 2004 and 2020. Our study focuses on the summer when strong planetary waves are absent and the stratospheric and tropospheric conditions are relatively stable. We establish two definitions of the summer length according to the zonal wind reversals: (1) the mesosphere and lower thermosphere summer length (MLT-SL) using SMR and PRR winds, and (2) the mesosphere summer length (M-SL) using PRR and MLS. Under both definitions, the summer begins around April and ends around mid-September. The largest year to year variability is found in the summer beginning in both definitions, particularly at high-latitudes, possibly due to the influence of the polar vortex. At high-latitudes, the year 2004 has a longer summer length compared to the mean value for MLT-SL, as well as 2012 for both definitions. The M-SL exhibits an increasing trend over the years, while MLT-SL does not have a well-defined trend. We explore a possible influence of solar activity, as well as large-scale atmospheric influences (e.g. quasi-biennial oscillations (QBO), El Niño-southern oscillation (ENSO), major sudden stratospheric warming events). We complement our work with an extended time series of 31 years at mid-latitudes using only PRR winds. In this case, the summer length shows a breakpoint, suggesting a non-uniform trend, and periods similar to those known for ENSO and QBO.

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