Abstract

The response of air temperatures to widespread irrigation may represent an important component of past and/or future regional climate changes. The quantitative impact of irrigation on daily minimum and maximum temperatures (T _min and T _max) in California was estimated using historical time series of county irrigated areas from agricultural censuses and daily climate observations from the U.S. Historical Climatology Network. Regression analysis of temperature and irrigation changes for stations within irrigated areas revealed a highly significant (p < 0.01) effect of irrigation on June–August average T _max, with no significant effects on T _min (p > 0.3). The mean estimate for T _max was a substantial 5.0°C cooling for 100% irrigation cover, with a 95% confidence interval of 2.0°–7.9°C. As a result of small changes in T _min compared to T _max, the diurnal temperature range (DTR) decreased significantly in both spring and summer months. Effects on percentiles of T _max within summer months were not statistically distinguishable, suggesting that irrigation’s impact is similar on warm and cool days in California. Finally, average trends for stations within irrigated areas were compared to those from nonirrigated stations to evaluate the robustness of conclusions from previous studies based on pairwise comparisons of irrigated and nonirrigated sites. Stronger negative T _max trends in irrigated sites were consistent with the inferred effects of irrigation on T _max. However, T _min trends were significantly more positive for nonirrigated sites despite the apparent lack of effects of irrigation on T _min from the analysis within irrigated sites. Together with evidence of increases in urban areas near nonirrigated sites, this finding indicates an important effect of urbanization on T _min in California that had previously been attributed to irrigation. The results therefore demonstrate that simple pairwise comparisons between stations in a complex region such as California can lead to misinterpretation of historical climate trends and the effects of land use changes.

Abstract

The Indian Ocean monsoon (IOM) exhibits considerable year-to-year variations that have previously been attributed to a number of forcing mechanisms including the El Niño–Southern Oscillation (ENSO) and Eurasian snow cover anomalies. In this study, spatial data of Eurasian spring land surface temperatures are analyzed as well as proxies for soil moisture, summer IOM precipitation, and summer IOM 850-mb zonal winds for the 1979–99 period to isolate correlated modes of variability. The results indicate the existence of a prominent mode that appears to be related to the boreal winter Arctic Oscillation (AO); this mode projects strongly on the June precipitation and 850-mb zonal wind fields in the vicinity of the IOM region. Its projection on spatial fields of temperature and proxies for soil moisture shows springtime surface warming and drying in the region to the north and west of the Indian subcontinent and cooling over the higher Eurasian latitudes during years of anomalously intense June monsoon rainfall. Such surface signatures are consistent with the negative phase winter AO. It is hypothesized that the preconditioning of the spring season surface characteristics may be associated with an AO-induced quasi-stationary tropospheric circulation anomaly: the impact of this anomaly is to displace the mid-Eastern jet poleward during AO-negative phases, resulting in anomalous surface heating and drying that persist into the later spring season and finally affect the rainfall over the IOM region in June.

Abstract

Expansion of irrigated land can cause local cooling of daytime temperatures by up to several degrees Celsius. Here the authors compare the expected cooling associated with rates of irrigation expansion in developing countries for historical (1961–2000) and future (2000–30) periods with climate model predictions of temperature changes from other forcings, most notably increased atmospheric greenhouse gas levels, over the same periods. Indirect effects of irrigation on climate, via methane production in paddy rice systems, were not considered. In regions of rapid irrigation growth over the past 40 yr, such as northwestern India and northeastern China, irrigation’s expected cooling effects have been similar in magnitude to climate model predictions of warming from greenhouse gases. A masking effect of irrigation can therefore explain the lack of significant increases in observed growing season maximum temperatures in these regions and the apparent discrepancy between observations and climate model simulations. Projections of irrigation for 2000–30 indicate a slowing of expansion rates, and therefore cooling from irrigation expansion over this time period will very likely be smaller than in recent decades. At the same time, warming from greenhouse gases will likely accelerate, and irrigation will play a relatively smaller role in agricultural climate trends. In many irrigated regions, therefore, temperature projections from climate models, which generally ignore irrigation, may be more accurate in predicting future temperature trends than their performance in reproducing past observed trends in irrigated regions would suggest.

Abstract

Many models in the framework of the Paleoclimate Modelling Intercomparison Project have undertaken simulations of the mid-Holocene (6 kyr ago) climate change. Analysis of the results have mainly focused on the North African summer monsoon that was enhanced 6 kyr ago, in all models, in response to the prescribed enhanced summer insolation. The magnitude of the simulated increase in total rainfall is very different, however, among the models, and so is the prescribed mean hot desert albedo, which varies from 19% to 38%. The appropriate prescription of hot desert's brightness, in the simulation of present-day climate, is known to be a key parameter since the work of Charney, which has been confirmed by many subsequent studies. There is yet no consensus, however, on the albedo climatological values to be used by climate modelers. Here, it is questioned whether changes in the prescription of hot desert albedo may also affect the simulated intensity of climate change.

Using the Laboratoire de Météorologie Dynamique atmospheric general circulation model, two sets of simulations, with a mean hot desert albedo of respectively 35% and 28%, have been carried out. The simulated mid-Holocene summer monsoon change in northern Africa is significantly larger when the background hot desert albedo is the lowest (i.e., 28%). The associated increased northward penetration of monsoon rains allows a greater reduction of hot desert area that is in better agreement with paleodata. At least three good reasons have been found to explain these changes, one of them being that when hot desert albedo is relatively low, the atmosphere above is more unstable and the same increase in solar forcing leads to larger changes in precipitable water. The implication of such a study is that differences in models' responses to any external forcing (insolation, increased atmospheric CO₂, etc.) may be partly explained by differences in the prescription of land surface properties. The interpretation of climate change resulting from only one model must therefore be taken with great care.

Abstract

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.

Abstract

Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.

Abstract

Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP and PATMOS-x. The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.

Abstract

El Niño–Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Abstract

The 2011–16 California drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño–Southern Oscillation (ENSO) returns. In the twenty-first century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the changes in aridity driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming–induced increase in evaporative demand. Here, atmosphere-only experiments are analyzed to identify land regions where aridity is currently sensitive to ENSO and where projected future changes in mean aridity exceed the range caused by ENSO variability. Insights into the drivers of these changes in aridity are obtained using simulations with the incremental addition of three different factors to the current climate: ocean warming, vegetation response to elevated CO₂ levels, and intensified CO₂ radiative forcing. The effect of ocean warming overwhelms the range of ENSO-driven temperature variability worldwide, increasing potential evapotranspiration (PET) in most ENSO-sensitive regions. Additionally, about 39% of the regions currently sensitive to ENSO will likely receive less precipitation in the future, independent of the ENSO phase. Consequently aridity increases in 67%–72% of the ENSO-sensitive area. When both radiative and physiological effects are considered, the area affected by arid conditions rises to 75%–79% when using PET-derived measures of aridity, but declines to 41% when an aridity indicator for total soil moisture is employed. This reduction mainly occurs because plant stomatal resistance increases under enhanced CO₂ concentrations, resulting in improved plant water-use efficiency, and hence reduced evapotranspiration and soil desiccation. Imposing CO₂-invariant stomatal resistance may overestimate future drying in PET-derived indices.