scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
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.

scholarly journals Satellite-Observed Surface Temperature Changes after the 2004 Taylor Complex Fire in Alaska

2011 ◽  
Vol 15 (11) ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ge
Keyword(s):  
Surface Temperature ◽  
Boreal Forest ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Primary Data ◽  
Burned Area ◽  
Area Index ◽  
Surface Temperature Change ◽  
Temperature Changes

Abstract Land-use and land-cover change has been recognized as a key component in global climate change. In the boreal forest ecosystem, fires often cause significant changes in vegetation structure and surface biophysical characteristics, which in turn dramatically change energy and water balances of land surface. Several studies have characterized fire-induced changes in surface energy balance in boreal ecosystem based on site observations. This study provides satellite-observed impacts of a large fire on surface climate in Alaska’s boreal forest. A land surface temperature (LST) product from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the primary data. Five years after fire, surface temperature over the burned area increased by an average of 2.0°C in the May–August period. The increase reached a maximum of 3.2°C in the year immediately following the fire. The warm anomaly decreased slightly after the second year but remained until the fifth year of the study. These changes in surface temperature may directly affect surface net radiation and thus partition of surface available energy. By documenting continuous and synoptic surface temperature changes over multiple years, this paper demonstrates the value of Earth Observing System (EOS) observations for land–climate interaction research. The observed characteristics of surface temperature change as well as changes in key surface biophysical parameters such as albedo and leaf area index (LAI) can be used in the next generation of climate models to improve the representation of large-scale ecosystem disturbances.

scholarly journals The construction of a Central Netherlands temperature

2011 ◽  
Vol 7 (2) ◽  
pp. 527-542 ◽  
Author(s):  
G. van der Schrier ◽  
A. van Ulden ◽  
G. J. van Oldenborgh
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Homogeneity Analysis ◽  
Temperature Changes ◽  
Long Time Series ◽  
Scale Temperature ◽  
Long Time

Abstract. The Central Netherlands Temperature (CNT) is a monthly daily mean temperature series constructed from homogenized time series from the centre of the Netherlands. The purpose of this series is to offer a homogeneous time series representative of a larger area in order to study large-scale temperature changes. It will also facilitate a comparison with climate models, which resolve similar scales. From 1906 onwards, temperature measurements in the Netherlands have been sufficiently standardized to construct a high-quality series. Long time series have been constructed by merging nearby stations and using the overlap to calibrate the differences. These long time series and a few time series of only a few decades in length have been subjected to a homogeneity analysis in which significant breaks and artificial trends have been corrected. Many of the detected breaks correspond to changes in the observations that are documented in the station metadata. This version of the CNT, to which we attach the version number 1.1, is constructed as the unweighted average of four stations (De Bilt, Winterswijk/Hupsel, Oudenbosch/Gilze-Rijen and Gemert/Volkel) with the stations Eindhoven and Deelen added from 1951 and 1958 onwards, respectively. The global gridded datasets used for detecting and attributing climate change are based on raw observational data. Although some homogeneity adjustments are made, these are not based on knowledge of local circumstances but only on statistical evidence. Despite this handicap, and the fact that these datasets use grid boxes that are far larger then the area associated with that of the Central Netherlands Temperature, the temperature interpolated to the CNT region shows a warming trend that is broadly consistent with the CNT trend in all of these datasets. The actual trends differ from the CNT trend up to 30 %, which highlights the need to base future global gridded temperature datasets on homogenized time series.

scholarly journals Different response of surface temperature and air temperature to deforestation in climate models

2019 ◽  
Vol 10 (3) ◽  
pp. 473-484 ◽  
Author(s):  
Johannes Winckler ◽  
Christian H. Reick ◽  
Sebastiaan Luyssaert ◽  
Alessandro Cescatti ◽  
Paul C. Stoy ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Large Scale ◽  
Model Comparison ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Nonlocal Effects ◽  
Local Effects ◽  
Near Surface ◽  
Temperature Changes

Abstract. When quantifying temperature changes induced by deforestation (e.g., cooling in high latitudes, warming in low latitudes), satellite data, in situ observations, and climate models differ concerning the height at which the temperature is typically measured/simulated. In this study the effects of deforestation on surface temperature, near-surface air temperature, and lower atmospheric temperature are compared by analyzing the biogeophysical temperature effects of large-scale deforestation in the Max Planck Institute Earth System Model (MPI-ESM) separately for local effects (which are only apparent at the location of deforestation) and nonlocal effects (which are also apparent elsewhere). While the nonlocal effects (cooling in most regions) influence the temperature of the surface and lowest atmospheric layer equally, the local effects (warming in the tropics but a cooling in the higher latitudes) mainly affect the temperature of the surface. In agreement with observation-based studies, the local effects on surface and near-surface air temperature respond differently in the MPI-ESM, both concerning the magnitude of local temperature changes and the latitude at which the local deforestation effects turn from a cooling to a warming (at 45–55∘ N for surface temperature and around 35∘ N for near-surface air temperature). Subsequently, our single-model results are compared to model data from multiple climate models from the Climate Model Intercomparison Project (CMIP5). This inter-model comparison shows that in the northern midlatitudes, both concerning the summer warming and winter cooling, near-surface air temperature is affected by the local effects only about half as strongly as surface temperature. This study shows that the choice of temperature variable has a considerable effect on the observed and simulated temperature change. Studies about the biogeophysical effects of deforestation must carefully choose which temperature to consider.

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

scholarly journals Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1551
Author(s):  
Jiaqi Zhang ◽  
Xiangjin Shen ◽  
Yanji Wang ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Changbai Mountain ◽  
Vegetation Coverage ◽  
Area Index ◽  
Forest Coverage ◽  
Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals Influence of the representation of convection on the mid-Holocene West African Monsoon

2021 ◽  
Vol 17 (4) ◽  
pp. 1665-1684
Author(s):  
Leonore Jungandreas ◽  
Cathy Hohenegger ◽  
Martin Claussen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Monsoon Season ◽  
West African Monsoon ◽  
West African ◽  
Global Climate Models ◽  
African Monsoon ◽  
Monsoonal Precipitation

Abstract. Global climate models experience difficulties in simulating the northward extension of the monsoonal precipitation over north Africa during the mid-Holocene as revealed by proxy data. A common feature of these models is that they usually operate on grids that are too coarse to explicitly resolve convection, but convection is the most essential mechanism leading to precipitation in the West African Monsoon region. Here, we investigate how the representation of tropical deep convection in the ICOsahedral Nonhydrostatic (ICON) climate model affects the meridional distribution of monsoonal precipitation during the mid-Holocene by comparing regional simulations of the summer monsoon season (July to September; JAS) with parameterized and explicitly resolved convection. In the explicitly resolved convection simulation, the more localized nature of precipitation and the absence of permanent light precipitation as compared to the parameterized convection simulation is closer to expectations. However, in the JAS mean, the parameterized convection simulation produces more precipitation and extends further north than the explicitly resolved convection simulation, especially between 12 and 17∘ N. The higher precipitation rates in the parameterized convection simulation are consistent with a stronger monsoonal circulation over land. Furthermore, the atmosphere in the parameterized convection simulation is less stably stratified and notably moister. The differences in atmospheric water vapor are the result of substantial differences in the probability distribution function of precipitation and its resulting interactions with the land surface. The parametrization of convection produces light and large-scale precipitation, keeping the soils moist and supporting the development of convection. In contrast, less frequent but locally intense precipitation events lead to high amounts of runoff in the explicitly resolved convection simulations. The stronger runoff inhibits the moistening of the soil during the monsoon season and limits the amount of water available to evaporation in the explicitly resolved convection simulation.

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