This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have ...altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144
This article is categorized under:
Paleoclimates and Current Trends > Climate Forcing
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The common methodology in dynamical regional climate downscaling employs a continuous integration of a limited‐area model with a single initialization of the atmospheric fields and frequent updates ...of lateral boundary conditions based on general circulation model outputs or reanalysis data sets. This study suggests alternative methods that can be more skillful than the traditional one in obtaining high‐resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically downscale the 1‐degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: (1) continuous integrations with a single initialization as usually done, (2) consecutive integrations with frequent re‐initializations, and (3) as (1) but with a 3‐D nudging being applied. The simulations are evaluated in a high temporal scale (6‐hourly) by comparison with the 32‐km NCEP North American Regional Reanalysis (NARR). Compared to NARR, the downscaling simulation using the 3‐D nudging shows the highest skill, and the continuous run produces the lowest skill. While the re‐initialization runs give an intermediate skill, a run with a more frequent (e.g., weekly) re‐initialization outperforms that with the less frequent re‐initialization (e.g., monthly). Dynamical downscaling outperforms bi‐linear interpolation, especially for meteorological fields near the surface over the mountainous regions. The 3‐D nudging generates realistic regional‐scale patterns that are not resolved by simply updating the lateral boundary conditions as done traditionally, therefore significantly improving the accuracy of generating regional climate information.
Land Use and Climate Change Pielke, Roger A.
Science (American Association for the Advancement of Science),
12/2005, Volume:
310, Issue:
5754
Journal Article
Peer reviewed
Open access
Change and variability in land use by humans and the resulting alterations in surface features are major but poorly recognized drivers of long-term global climate patterns. Along with the diverse ...influences of aerosols on climate, these spatially heterogeneous land use effects may be at least as important in altering the weather as changes in climate patterns associated with greenhouse gases. On page 1674 of this issue, Feddema et al. report modeling results indicating that future land use and land cover will continue to be an important influence on climate for the next century. One implication of this work is that the Intergovernmental Panel on Climate Change (IPCC), which has yet to appreciate the significance of the full range of phenomena that drive climate change, risks rapidly falling behind the evolving science if this effect is not included. Although the impact of land use and land cover on the atmospheric concentration of carbon dioxide and methane, and on the global average surface albedo, have been included in international climate change assessments, the role of land use and land cover change and variability in altering regional temperatures, precipitation, vegetation, and other climate variables has been mostly ignored.
Full text
Available for:
BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Global responses of the hydrological cycle to climate change have been widely studied, but uncertainties still remain regarding water vapor responses to lower-tropospheric temperature. Here, we ...investigate the trends in global total precipitable water (TPW) and surface temperature from 1958 to 2021 using ERA5 and JRA-55 reanalysis datasets. We further validate these trends using radiosonde from 1979 to 2019 and Atmospheric Infrared Sounder (AIRS) and Special Sensor Microwave Imager/Sounder (SSMIS) observations from 2003 to 2021. Our results indicate a global increase in total precipitable water (TPW) of ∼ 2 % per decade from 1993–2021. These variations in TPW reflect the interactions of global warming feedback mechanisms across different spatial scales. Our results also revealed a significant near-surface temperature (T2 m) warming trend of ∼ 0.15 K decade−1 over the period 1958–2021. The consistent warming at a rate of ∼ 0.21 K decade−1 after 1993 corresponds to a strong water vapor response to temperature at a rate of 9.5 % K−1 globally, with land areas warming approximately twice as fast as the oceans. The relationship between TPW and T2 m showed a variation of around 6 % K−1–8 % K−1 in the 15–55° N latitude band, aligning with theoretical estimates from the Clausius–Clapeyron equation.
This 2007 edition of Human Impacts on Weather and Climate examines the scientific and political debates surrounding anthropogenic impacts on the Earth's climate and presents the most recent theories, ...data and modeling studies. The book discusses the concepts behind deliberate human attempts to modify the weather through cloud seeding, as well as inadvertent modification of weather and climate on the regional scale. The natural variability of weather and climate greatly complicates our ability to determine a clear cause-and-effect relationship to human activity. The authors describe the basic theories and critique them in simple and accessible terms. This fully revised edition will be a valuable resource for undergraduate and graduate courses in atmospheric and environmental science, and will also appeal to policy makers and general readers interested in how humans are affecting the global climate.
The value restored and added by dynamical downscaling is quantitatively evaluated by considering the spectral behavior of the Regional Atmospheric Modeling System (RAMS) in relation to its domain ...size and grid spacing. A regional climate model (RCM) simulation is compared with NCEP Reanalysis data regridded to the RAMS grid at each model analysis time for a set of six basic experiments. At large scales, RAMS underestimates atmospheric variability as determined by the column integrated kinetic energy and integrated moisture flux convergence. As the grid spacing increases or domain size increases, the underestimation of atmospheric variability at large scales worsens. The model simulated evolution of the kinetic energy relative to the reanalysis regridded kinetic energy exhibits a decrease with time, which is more pronounced with larger grid spacing. Additional follow‐on experiments confirm that the surface boundary forcing is the dominant factor in generating atmospheric variability for small‐scale features and that it exerts greater control on the RCM solution as the influence of lateral boundary conditions diminish. The sensitivity to surface forcing is also influenced by the model parameterizations, as demonstrated by using a different convection scheme. For the particular case considered, dynamical downscaling with RAMS in RCM mode does not retain value of the large scale which exists in the larger global reanalysis. The utility of the RCM, or value added, is to resolve the smaller‐scale features which have a greater dependence on the surface boundary. This conclusion regarding RAMS is expected to be true for other RCMs as well.
In this paper, we compare the retained and added variability obtained using the regional climate model CLM (Climate version of the Local Model of the German Weather Service) to an earlier study using ...the RAMS (Regional Atmospheric Modeling System) model. Both models yield similar results for their standard configurations with a commonly used nudging technique applied to the driving model fields. Significantly both models do not adequately retain the large‐scale variability in total kinetic energy with results poorer on a larger grid domain. Additional experiments with interior nudging, however, permit the retention of large‐scale values for both models. The spectral nudging technique permits more added variability at smaller scales than a four‐dimensional internal grid nudging on large domains. We also confirmed that dynamic downscaling does not retain (or increase) simulation skill of the large‐scale fields over and beyond that which exists in the larger‐scale model or reanalysis. Our conclusions should be relevant to all applications of dynamic downscaling for regional climate simulations.
•Irrigation changes precipitation.•Increased irrigation produces shallower planetary boundary layer.•Increased irrigation increases latent heat flux and reduces sensible heat flux.•Increased ...irrigation increases equivalent temperature and reduces air temperature.
Land use land cover change, including irrigation, impacts weather and climate. In this paper a precipitation event that occurred on the morning of 23 July 2018 during the Great Plains Irrigation Experiment (GRAINEX) is investigated. Six Weather and Research Forecasting (WRF) model-based experiments were conducted, which involved the increase or decrease of soil moisture by 5 % and up to 15 % over the irrigated croplands. These changes approximated soil moisture content in response to different levels of irrigation applications. An additional experiment, where irrigated land use was changed to grassland, was conducted to capture pre-irrigation land use and its impacts. It was found that regardless of strength of irrigation, precipitation decreased. In addition, the model did not produce precipitation over non-irrigated land use. When grassland replaced irrigated agriculture, increases in precipitation were estimated. With increased irrigation, latent heat flux increased compared to the control simulation and decreased when irrigation decreased. On the other hand, sensible heat flux was decreased compared to control when irrigation increased. The planetary boundary layer over irrigated land use was shallower than over non-irrigated land use while over grassland it was higher than irrigated but lower than non-irrigated land use. The changes in precipitation, the surface energy balance, and the planetary boundary layer served as a reminder of irrigation's complex effects on the atmosphere. Additional analysis of other precipitation events during GRAINEX would be helpful to better understand the effects of irrigation.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The recently concluded Surface Stations Project surveyed 82.5% of the U.S. Historical Climatology Network (USHCN) stations and provided a classification based on exposure conditions of each surveyed ...station, using a rating system employed by the National Oceanic and Atmospheric Administration to develop the U.S. Climate Reference Network. The unique opportunity offered by this completed survey permits an examination of the relationship between USHCN station siting characteristics and temperature trends at national and regional scales and on differences between USHCN temperatures and North American Regional Reanalysis (NARR) temperatures. This initial study examines temperature differences among different levels of siting quality without controlling for other factors such as instrument type. Temperature trend estimates vary according to site classification, with poor siting leading to an overestimate of minimum temperature trends and an underestimate of maximum temperature trends, resulting in particular in a substantial difference in estimates of the diurnal temperature range trends. The opposite‐signed differences of maximum and minimum temperature trends are similar in magnitude, so that the overall mean temperature trends are nearly identical across site classifications. Homogeneity adjustments tend to reduce trend differences, but statistically significant differences remain for all but average temperature trends. Comparison of observed temperatures with NARR shows that the most poorly sited stations are warmer compared to NARR than are other stations, and a major portion of this bias is associated with the siting classification rather than the geographical distribution of stations. According to the best‐sited stations, the diurnal temperature range in the lower 48 states has no century‐scale trend.
Key Points
Temperature trend estimates vary according to site classification
Poorly sited stations are warmer compared to interpolated NARR temperatures
The diurnal temperature range in the lower 48 states has no century‐scale trend
Catastrophic events share characteristic nonlinear behaviors that are often generated by cross-scale interactions and feedbacks among system elements. These events result in surprises that cannot ...easily be predicted based on information obtained at a single scale. Progress on catastrophic events has focused on one of the following two areas: nonlinear dynamics through time without an explicit consideration of spatial connectivity Holling, C. S. (1992) Ecol. Monogr. 62, 447-502 or spatial connectivity and the spread of contagious processes without a consideration of crossscale interactions and feedbacks Zeng, N., Neeling, J. D., Lau, L. M. & Tucker, C. J. (1999) Science 286, 1537-1540. These approaches rarely have ventured beyond traditional disciplinary boundaries. We provide an interdisciplinary, conceptual, and general mathematical framework for understanding and forecasting nonlinear dynamics through time and across space. We illustrate the generality and usefulness of our approach by using new data and recasting published data from ecology (wildfires and desertification), epidemiology (infectious diseases), and engineering (structural failures). We show that decisions that minimize the likelihood of catastrophic events must be based on cross-scale interactions, and such decisions will often be counterintuitive. Given the continuing challenges associated with global change, approaches that cross disciplinary boundaries to include interactions and feedbacks at multiple scales are needed to increase our ability to predict catastrophic events and develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing experiments to examine cross-scale interactions.
Full text
Available for:
BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
PDF
