Antecedent Climate Conditions Research Articles

Seasonal climate summary for the southern hemisphere (summer 2019–20): a summer of extremes

This is a summary of the southern hemisphere atmospheric circulation patterns and meteorological indices for summer 2019–20; an account of seasonal rainfall and temperature for the Australian region is also provided. The antecedent climate conditions and climatic drivers for summer 2019–20 resulted in unprecedented extremes for Australia, with many heat and fire weather extremes. The austral summer of 2019–20 was staged to be hot and dry, with climate drivers supporting higher than average temperatures and lower than average rainfall. These conditions contributed to the highest recorded monthly accumulated national Forest Fire Danger Index. As the dominant climate influence for December receded during the season, dynamic (weather) processes dominated for changeable conditions – particularly in the mid-latitudes. Both January and February were among the 10 hottest on record, although several mid-latitude sites experienced unusually cool days. Across the rest of the hemisphere, conditions were also extreme, with notable drought conditions persisting from spring over large parts of South America. Temperature anomalies for land and ocean areas of the southern hemisphere were respectively the third and second highest on record.

Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Climate Dynamics Preceding Summer Forest Fires in California and the Extreme Case of 2018

Abstract Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years. Significance Statement The purpose of this study is to identify the local and global climate patterns in the preceding seasons that influence how the burned summer forest area in California varies year-to-year. We find that a dry atmosphere, high temperatures, dry soils, less snowpack, low precipitation, subsiding air, and high pressure centered west of California all correlate significantly with large summer burned area as far back as the preceding January. These climate anomalies occur as part of a hemispheric scale pattern with weak connections to the tropical Pacific Ocean. We also describe the climate anomalies preceding the extreme and record-breaking burned-area year of 2018, and how these compared with the more general patterns found. These results give important insight into how well and how early it might be possible to predict the severity of an upcoming summer wildfire season in California.

Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area

This study predicts summer (June–September) fire burned area across the western United States (U.S.) from 1984 to 2020 using ensembles of statistical models trained with pre-fire season climate conditions. Winter and spring climate conditions alone explain up to 53% of the interannual variability and 58% of the increasing trend of observed summer burned area, which suggests that climate conditions in antecedent seasons have been an important driver to broad-scale changes in summer fire activity in the western U.S. over the recent four decades. Relationships between antecedent climate conditions with summer burned area are found to be strongest over non-forested and middle-to-high elevation areas (1100–3300 m). Statistical models that predict summer burned area using both antecedent and fireseason climate conditions have improved performance, explaining 69% of the interannual variability and 83% of the increasing trend of observed burned area. Among the antecedent climate predictors, vapor pressure deficit averaged over winter and spring plays the most critical role in predicting summer fire burned area. Spring snow drought area is found to be an important antecedent predictor for summer burned area over snow-reliant regions in the nonlinear statistical modeling framework used in this analysis. Namely, spring snow drought memory is realized through dry anomalies in land (soil and fuel) and atmospheric moisture during summer, which favours fire activity. This study highlights the important role of snow drought in subseasonal-to-seasonal forecasts of summer burned area over snow-reliant areas.

Past fires and post-fire impacts reconstructed from a southwest Australian stalagmite

Speleothem records of past environmental change provide an important opportunity to explore fire frequency and intensity in the past, and the antecedent climatic conditions leading to fire events. Here, fire sensitive geochemical signals in a stalagmite from Yonderup Cave, a shallow cave in Western Australia, are compared to well-documented wildfire events that occurred in recent decades. The results are extended to identify wildfires during the growth interval of the speleothem (1760 CE–2005 CE). Principal component analysis of the stalagmite time-series revealed distinct peaks in a combination of phosphorus and metal (aluminium, zinc, copper and lead) concentrations in response to known fire events, which are interpreted to have come from ash. Varying responses in the geochemical signal in the pre- and post-European colonisation period are likely linked to changes in land management, fire frequency and fire intensities. Of note is what we infer to be a particularly intense fire event concluded to have occurred in 1897 ± 5 CE, which climate sensitive proxies (δ18O and bedrock-derived elements) indicate was preceded by a multi-decadal dry period that began in the late 1860s. The intensity of the deduced fire event is supported by a peak in P that is 6.3 times higher than elsewhere in the speleothem, accompanied by a peak in Zn. It was also inferred that changes to the surface-cave hydrology occurred as a result of this event, indicated by increased input of colloidal organic material onto the stalagmite that was interpreted to be caused by shallow karst bedrock fracturing from the heat-induced deformation that is observed after intense fires. These findings show the potential for speleothems to provide dated records of fire intensity and recurrence intervals. Further development could lead to a better understanding of the climate-fire relationship and the effects of land-management practices on wildfire frequency and intensity.

Compound climate extremes driving recent sub-continental tree mortality in northern Australia have no precedent in recent centuries

Compound climate extremes (CCEs) can have significant and persistent environmental impacts on ecosystems. However, knowledge of the occurrence of CCEs beyond the past ~ 50 years, and hence their ecological impacts, is limited. Here, we place the widespread 2015–16 mangrove dieback and the more recent 2020 inland native forest dieback events in northern Australia into a longer historical context using locally relevant palaeoclimate records. Over recent centuries, multiple occurrences of analogous antecedent and coincident climate conditions associated with the mangrove dieback event were identified in this compilation. However, rising sea level—a key antecedent condition—over the three decades prior to the mangrove dieback is unprecedented in the past 220 years. Similarly, dieback in inland forests and savannas was associated with a multi-decadal wetting trend followed by the longest and most intense drought conditions of the past 250 years, coupled with rising temperatures. While many ecological communities may have experienced CCEs in past centuries, the addition of new environmental stressors associated with varying aspects of global change may exceed their thresholds of resilience. Palaeoclimate compilations provide the much-needed longer term context to better assess frequency and changes in some types of CCEs and their environmental impacts.

The spatial patterns of climate-fire relationships on the Mongolian Plateau

Climate widely influences the geographical patterns of wildfires in terrestrial ecosystems. Understanding climate-fire relationships is crucial in exploring the response of wildfires to climatic conditions in different ecogeographic environments. However, how climate affects regional wildfires and which factors dominate the spatial patterns of climate-fire relationships are still poorly understood. Using satellite-based burned area data and climate datasets of the Mongolian Plateau, we analyzed the concurrent and antecedent climate-fire relationships; evaluated the spatial change characteristics of the climate-fire relationships along gradients of moisture, fuel, topography, and human activity; and estimated the ability of climate to explain fire dynamics to identify key factors mediating the spatial patterns of climate-fire relationships at ecoregion scales. The results showed that concurrent hot and dry and antecedent warm climatic conditions widely facilitated larger burned areas, and humid conditions increased the amount of burned areas in most arid ecoregions by controlling fuel accumulation. The strength of the climate-fire relationships changed along the environmental factor gradients, and the nature of climate-fire relationships shifted when variation in drivers exceeded the gradient thresholds. Wildfires respond differently to climatic conditions, and ecoregion-based models revealed that climate explained 21.53% of the variation in burned areas on average. These findings suggested that under universal arid environments, climate across multiple time scales shapes distinct local fire regimes by controlling fuel flammability and fuel accumulation, and vegetation productivity and moisture mediate the spatial patterns of climate-fire relationships across ecoregions. This study provides knowledge of climate-fire interactions in mid-latitude regions at ecoregion scales and facilitates policies for regional fire suppression.

Climate and disturbance influence self-sustaining stand dynamics of aspen (Populus tremuloides) near its range margin.

Species that are primarily seral may form stable (self-sustaining) communities under certain disturbance regimes or environmental conditions, yet such populations may also be particularly vulnerable to ecological change. Aspen (Populus spp.) are generally considered seral throughout the Northern Hemisphere, including P.tremuloides, the most widely distributed tree species in North America. Recent declines in aspen populations have occurred, especially along drought-sensitive margins of its range and where fire exclusion and herbivory have promoted community transition. However, aspen also forms stable stands, and examination of the mechanisms that influence persistence can offer conservation insights, especially where populations are vulnerable to changing climate or altered disturbance dynamics. We sampled tree age and stand characteristics of isolated aspen forests in the arid Great Basin (USA) to determine if (1) aspen communities are more fire-dependent and seral or fire-independent and stable; (2) ungulate browsing inhibits aspen stability; and (3) temporal patterns of vegetative reproduction (i.e., ramet establishment or "suckering") are correlated with climate. Aspen size and age class densities strongly fit negative exponential distributions, whether grouped geographically or by functional type, suggesting landscape-scale persistence. Continuous age distributions and high proportions of recruitment-sized to overstory trees suggest stability at stand scales, with exceptions including stands with higher browsing pressure. Few stands had evidence of fire, and relationships between dead tree size and variability in live tree size suggest a lack of fire dependency. Several 5-yr averaged climate variables and one sea surface temperature index were correlated with aspen ramet establishment densities over time, with strongest relationships occurring ~5yr prior to establishment year, often followed by inverse relationships ~1yr after. Indeed, aspen establishment density for a recent 41-yr period was reliably reconstructed using antecedent climate conditions derived from a single drought index. Temporally synchronized aspen ramet establishment across the study region may be due to climate-driven storage of nonstructural carbohydrate reserves in clonal root systems later used for regeneration. Complex regeneration dynamics of these self-sustaining aspen stands, especially sensitivity to climate variability, suggest they may serve as harbingers of ecological change in the arid Great Basin and in other aspen populations near their range margin.

Investigating the Relationship Between Climate and Valley Fever (Coccidioidomycosis)

Valley fever (coccidioidomycosis) is a disease caused by inhalation of spores from the soil-dwelling Coccidioides fungal species. The disease is endemic to semiarid areas in the western USA and parts of Central and South America. The region of interest for this study, Kern County, California, accounts for approximately 14% of the reported valley fever cases in the USA each year. It is hypothesized that the weather conditions that foster the growth and dispersal of the fungus influence the number of cases in the endemic area. This study uses regression-based analysis to model and assess the seasonal relationships between valley fever incidence and climatic variables including concurrent and lagged precipitation, temperature, Palmer Drought Severity Index, wind speed, and PM10 using data from 2000 to 2015. We find statistically significant links between disease incidence and climate conditions in Kern County, California. The best performing seasonal model explains up to 76% of the variability in fall valley fever incidence based on concurrent and antecedent climate conditions. Findings are consistent with previous studies, suggesting that antecedent precipitation is an important predictor of disease. The significant relationships found support the "grow and blow" hypothesis for climate-related coccidioidomycosis incidence risk that was originally developed for Arizona.

Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

AbstractSeasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large‐scale climate indices as predictors in statistical models. In contrast, hybrid statistical‐dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon, and their skill is largely unknown. Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.

Water memory effects and their impacts on global vegetation productivity and resilience

Memory effects refer to the impacts of antecedent climate conditions on current vegetation productivity. This temporal linkage has been found to be strong in arid and semi-arid regions. However, the dominant climatic factors that determine such patterns are still unclear. Here, we defined’water-memory effects’ as the persistent effects of antecedent precipitation on the vegetation productivity for a given memory length (from 1 to up to 12 months). Based on satellite observations and climate data, we quantified the length of water-memory effects and evaluated the contributions of antecedent precipitation on current vegetation. Our results showed that vegetation productivity was highly dependent on antecedent precipitation in arid and semi-arid regions. The average length of water memory was approximately 5.6 months. Globally, water-memory effects could explain the geographical pattern and strength of memory effects, indicating that precipitation might be the dominant climatic factor determining memory effects because of its impact on water availability. Moreover, our results showed vegetation in regions with low mean annual precipitation or a longer water memory has lower engineering resilience (i.e. slower recovery rate) to disturbances. These findings will enable better assessment of memory effects and improve our understanding of the vulnerability of vegetation to climate change.

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America.

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread.

Quantifying antecedent climatic drivers of tree growth in the Southwestern US

Abstract Variation in antecedent (past) climate conditions is likely to govern tree growth over long periods of time. Antecedent conditions are rarely considered in models of tree growth, representing a weakness in quantitative understanding of forest responses to climate variations. We applied the stochastic antecedent modelling (SAM) framework to 367 International Tree Ring Data Bank chronologies in the southwestern US (“Southwest”) representing eight conifer species. To better understand climatic and physiologic controls on tree growth, we quantify the effects of antecedent precipitation, temperature and Palmer Drought Severity Index (PDSI) over 60 months preceding and including the year of ring formation. In Pinus edulis, Pinus ponderosa and Pseudotsuga menziesii, growth responded primarily to recent precipitation and temperature conditions (43%–49% of the response was driven by conditions during the year of ring formation), but to less recent PDSI conditions (>50% of response driven by conditions 13–48 months prior to the year of ring formation), though PDSI significantly affected growth at only 21% of sites. Combining extensive tree‐ring data with monthly resolution climate data also reveals key climatic events, such as the effect of monsoon arrival date on growth, especially in P. menziesii, highlighting the ability of the SAM framework to identify climate effects at multiple time‐scales. Sensitivity to antecedent climate, baseline growth at average climate conditions and the strength of first order autocorrelation varied spatially, suggesting variation in mean growing conditions, non‐structural carbohydrate storage, and/or seasonal precipitation contribution of the North American Monsoon may drive differences in growth sensitivities across species’ ranges. Synthesis. Our findings provide further evidence for multi‐year legacy effects of climate conditions, particularly drought metrics, on tree growth. Antecedent climate and especially drought are key drivers of growth in these species, and associated climatic sensitivities and growth indices vary spatially. We argue such factors should be considered in modelling efforts. The spatial variability in antecedent climate sensitivities points to key differences in how different populations within a species range may respond to climate change, particularly if timing of weather events, such as monsoon arrival date, or annual precipitation amounts undergoes significant changes.

On the key role of droughts in the dynamics of summer fires in Mediterranean Europe

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies.

A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

AbstractThis study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross‐validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out‐of‐sample summer low‐flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.

Climate and land-use effects on wildfire in northern Mexico, 1650–2010

Characterizing climate controls on fire regimes, and disentangling the effects of human relative to climate influences, has been difficult in forests of the western U.S. due to the nearly ubiquitous legacy of fire exclusion that began in the middle to late 19th century. However, the Sierra San Luis of northern Mexico, just across the border from Arizona and New Mexico, offers an opportunity to examine the influence of climate and land-use on fire history largely without the effects of modern fire exclusion. Pine forests in portions of the Sierra San Luis remain ungrazed and unlogged to this day, while other portions of the Sierra were not logged until ca. 1952–1954 or grazed until the early 1930s. Historical and modern fire regimes closely reflect these differences in land-use through time. Fires were relatively frequent in all sites until 1932, but continued at high frequency only in the sites without grazing or logging. Notably, the influence of drought and antecedent conditions for fires changed over time. From 1650 to 1886 (early period), fires occurred during drought years, with little influence of climate in antecedent years. However, from 1887 to 2003 (modern period), drought in the year of fire became generally unimportant and fires instead occurred following wet years. Above-average precipitation promotes accumulation of fine fuels, which apparently has been the primary constraint on fire ignition and spread in this semi-arid ecosystem during the modern period. Percentage of scarring aligned with multi-year fluctuations in Palmer Drought Severity Index (PDSI), with higher percent scarring in wet periods (X‾=19.371) and lower scarring in dry periods (X‾=13.778). Native American burning was not an important driver of past fire frequency, even though the study area lies within the historical homeland of the Chiricahua Apache people. We found no change in frequency of fires when Apaches were effectively removed in 1886. Climatic controls, rather than Apache wartime and peacetime periods, more easily explain changes in frequency over time. Projected increases in climate variability in the Southwest highlight the need to understand antecedent climate conditions conducive to fire occurrence in fuel-limited systems, including comparisons of historical to current climate–fire relationships. The relict forests of the Sierra San Luis, where fires continue to burn today with only minimal human interference, provide a rare look at these relationships.

Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent

This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.

Impact of climate variability on summer fires in a Mediterranean environment (northeastern Iberian Peninsula)

We analyse the impact of climate interannual variability on summer forest fires in Catalonia (northeastern Iberian Peninsula). The study period covers 25 years, from 1983 to 2007. During this period more than 16000 fire events were recorded and the total burned area was more than 240 kha, i.e. around 7.5% of whole Catalonia. We show that the interannual variability of summer fires is significantly correlated with summer precipitation and summer maximum temperature. In addition, fires are significantly related to antecedent climate conditions, showing positive correlation with lagged precipitation and negative correlation with lagged temperatures, both with a time lag of two years, and negative correlation with the minimum temperature in the spring of the same year. The interaction between antecedent climate conditions and fire variability highlights the importance of climate not only in regulating fuel flammability, but also fuel structure. On the basis of these results, we discuss a simple regression model that explains up to 76% of the variance of the Burned Area and up to 91% of the variance of the number of fires. This simple regression model produces reliable out-of-sample predictions of the impact of climate variability on summer forest fires and it could be used to estimate fire response to different climate change scenarios, assuming that climate-vegetation-humans-fire interactions will not change significantly.

Comment on “Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI) during 1900–2008” by Aiguo Dai

] Dai [2011] (henceforth D11) reported that the PalmerDrought Severity Index (PDSI) is superior to other statisti-cally based drought indices including the StandardizedPrecipitation Index (SPI) and the Standardized PrecipitationEvapotranspiration Index (SPEI). D11 argued that given thephysical character of the PDSI water balance model, theindex provides robust estimates of drought severity becauseit takes the preceding conditions into account, in contrast toother drought indices that are based purely on past statisticsof particular climate variable(s). However, D11 has over-estimated the ability of the PDSI to realistically simulate thedistributed soil water balance at large spatial scales, andignored the inherent complexity and multiscalar character ofdrought phenomena, which are related to more than themoisture conditions of the soil. In this comment we discussthe complex characteristics of droughts and the limitationsof the PDSI to quantify drought conditions in a variety ofhydrological systems. We describe the advantages of statis-tically based drought indices including the SPI and the SPEI.ThefactthattheSPIandtheSPEIarenot(anddonotintendtobe) physically based indices is more liberating than con-straining, especially when the physical basis of PDSI can beseriously questioned.[

Statistical Evaluation of Factors Affecting Indicator Bacteria in Urban Storm-Water Runoff

An urban watershed in Raleigh, North Carolina, was monitored for indicator bacteria during 20 rain events. Results showed elevated levels of E. coli, enterococci, and fecal coliform. Samples were compared based on seasonality and were found to be statistically different ( p<0.05 ) , with pairwise comparisons indicating significantly lower concentrations of E. coli and fecal coliform during the winter ( p<0.05 ) . Enterococci concentrations were substantially lower in the winter and fall, but no significant differences were found between seasons during pairwise comparisons ( p<0.05 ) . Correlation analyses showed multiple significant relationships between antecedent climate parameters, flow characteristics, and indicator bacteria concentrations. More detailed multiple linear regression yielded explanatory variables related to antecedent climate conditions. Variables were generally related to temperature and moisture conditions in the atmosphere and soil. The results of this study show indicator bacteria co...