The list below contains a collection of my recent publications. For the most up-to-date information see my CV.
Predictions of hydrological states and fluxes, especially transpiration, are poorly constrained in hydrological models due to large uncertainties in parameterization and process description. Novel technologies like remote sensing of sun-induced chlorophyll fluorescence (SIF)—which provides information from the photosynthetic apparatus—may help in constraining water cycle components. This paper discusses the nature of the plant physiological basis of the fluorescence signal and analyses the current literature linking hydrological states and fluxes to SIF. Given the connection between photosynthesis and transpiration, through the water use efficiency, SIF may serve as a pertinent constraint for hydrological models. The FLuorescence EXplorer (FLEX) satellite, planned to be launched in 2023, is expected to provide spatially high-resolution measurements of red and far-red SIF complementing the products from existing satellite missions and the high-temporal resolution products from upcoming geostationary missions. This new data stream may allow us to better constrain plant transpiration, assess the impacts of water stress on plants, and infer processes occurring in the root zone through the soil-plant water column. To make optimal use of this data, progress needs to be made in 1) our process representation of spatially aggregated fluorescence signals from spaceborne SIF instruments, 2) integration of fluorescence processes in hydrological models—particularly when paired with other satellite data, 3) quantifying the impact of soil moisture on SIF across scales, and 4) assessment of the accuracy of SIF measurements—especially from space.
To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation (Eo) perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing‐sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P) increases, and increased Eo reduces soil moisture. E and D are generally more elastic to changes in P than Eo. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices.
Plant water content observations using microwave remote sensing measurements allow monitoring of landscape‐scale plant water stress. During soil drying following rainfall events, we use a Granger causality framework to quantify the degree to which environmental factors drive satellite‐based plant water content loss across Africa’s diverse biomes. After soil drying into the water‐limited regime, satellite observations show that plants dry while solar radiation, vapor pressure deficit, and diurnal temperature amplitude increase. We find that soil drying primarily drives plant water content loss across African drylands, though with regional effects of diurnal temperature amplitude increases (found to indicate vapor pressure deficit increases here). We also detect interactions between these factors that reinforce plant drying during periods of soil moisture loss. Our results provide observational evidence across Africa that individual and interactive components of surface drying and heating can all drive plant water stress, especially during intermittent poststorm drying periods.
Microwave brightness temperature observations from the NASA Soil Moisture Active‐Passive (SMAP) mission and gauge‐based precipitation data over the US are used to reconstruct the soil water loss function and then historical (1979‐2019) hydrological fluxes in the form of evapotranspiration (ET) and drainage (D) are quantified. Over the period of study, with the exception of snowy and hyper‐arid regions, we observe a correlation of R2 > 0.6 between SMAP‐precipitation derived drainage estimates and streamflow measurements from the USGS. There is a bias between estimated drainage and USGS streamflow with an underestimation of about 1 [mm day‐1] in southwest US to 3 [mm day‐1] in parts of the eastern US. SMAP‐derived sensitivities of drainage and ET partitioning with respect to precipitation anomalies are also calculated. In parts of the Great Plains the drainage partitioning exhibits a near‐linear response, while in the southeast US, the response is nonlinear. Partitioning also is examined for 6 four‐digit hydrologic unit basins wherein year‐to‐year variations in drainage partitioning are shown to be key mediators in translating precipitation anomalies into streamflow anomalies. Observation‐driven drainage and ET estimates are obtained without relying on full hydrologic and Land Surface Models (LSMs). This independence (isolation from model parameterization assumptions) provides a path towards using satellite‐derived landscape hydrological diagnostics to assess hydrologic models and LSMs as well as to guide their further development.
Land surface energetic partitioning between latent, sensible, and ground heat fluxes determines climate and influences the terrestrial segment of land‐atmosphere coupling. Soil moisture, among other variables, has a direct influence on this partitioning. Dry surfaces characterize a water‐limited regime where evapotranspiration and soil moisture are coupled. This coupling is subdued for wet surfaces, or an energy‐limited regime. This framework is commonly evaluated using the evaporative fraction–‐soil moisture relationship. However, this relationship is explicitly or implicitly prescribed in land surface models. These impositions, in turn, confound model‐based evaluations of energetic partitioning‐–soil moisture relationships. In this study, we use satellite‐based observations of surface temperature diurnal amplitude (directly related to available energy partitioning) and soil moisture, free of model impositions, to estimate characteristics of surface energetic partitioning–‐soil moisture relationships during 10–‐20‐day surface drying periods across Africa. We specifically estimate the spatial patterns of water‐limited energy flux sensitivity to soil moisture (m) and the soil moisture threshold separating water and energy‐limited regimes (θ*). We also assess how time evolution of other factors (e.g., solar radiation, vapor pressure deficit, surface albedo, and wind speed) can confound the energetic partitioning–‐soil moisture relationship. We find higher m in drier regions and interestingly similar spatial θ* distributions across biomes. Vapor pressure deficit and insolation increases during drying tend to increase m. Only vapor pressure deficit increases in the Sahelian grasslands systematically decrease θ*. Ultimately, soil and atmospheric moisture availability together play the largest role in land surface energy partitioning with minimal consistent influences of time evolution of other forcings.
Evapotranspiration and photosynthetic carbon uptake are both constrained by water availability, with presumed water‐limited and water‐independent but energy‐limited regimes. We investigate water availability’s relationships with evaporative fluxes and photosynthetic measures at continental scale using surface and satellite‐based observations. The observation‐driven approach is independent of prior model‐based estimates. We find water‐limited and water‐independent regimes for both energy and carbon cycle processes in spatial and temporal aggregate, differences in water‐limitation relationships across ecohydrological regions, and more consistent signs of water limitation in photosynthetic than evaporative processes. Ecosystem water use efficiency’s response to water availability changes in sign with aridity state across time and space generally but shows negative correlations in temporal anomalies.
The partitioning of incident precipitation into evapotranspiration, runoff, drainage, storage change, and hydrologic fluxes depends on the soil moisture state. With the availability of global remotely sensed soil moisture fields, the functional dependence of each flux on soil moisture may be identifiable. In this study we develop an observation‐driven approach to map key hydroclimatology fields using remotely sensed soil moisture and gauge‐based precipitation data only. National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) low‐frequency microwave brightness temperature observations and precipitation fields, from the National Centers for Environmental Prediction, are the sole inputs into an adjoint‐state variational estimation framework. Furthermore, the proposed methodology does not rely on micrometeorological information, or land surface models. The approach is flexible by design so that almost any partitioning pattern can result from estimation and corresponding evapotranspiration and drainage fields can be quantified. Three‐year averaged summer season evapotranspiration estimates are compared with available vapor flux at in situ AmeriFlux eddy‐covariance sites. Basin‐averaged drainage over major U.S. hydrologic units is also compared with U.S. Geological Survey streamgages measurements. The remote sensing‐based estimated hydroclimate fields explain about 70% of the variance in the in situ measurements. This exploratory study adds to the body of evidence emerging in literature that a significant amount of hydrologic information is encoded in the dynamic fields of remotely sensed soil moisture. Observation‐driven hydroclimate data fields that are independent of land surface models can provide valuable insights into the state of the water cycle and guide future development of land surface models.
Surface soil moisture measurements are typically correlated to some degree with changes in subsurface soil moisture. We calculate a hydrologic length scale, λ, which represents (1) the mean‐state estimator of total column water changes from surface observations, (2) an e‐folding length scale for subsurface soil moisture profile covariance fall‐off, and (3) the best second‐moment mass‐conserving surface layer thickness for a simple bucket model, defined by the data streams of satellite soil moisture and precipitation retrievals. Calculations are simple, based on three variables: the autocorrelation and variance of surface soil moisture and the variance of the net flux into the column (precipitation minus estimated losses), which can be estimated directly from the soil moisture and precipitation time series. We develop a method to calculate the lag‐one autocorrelation for irregularly observed time series and show global surface soil moisture autocorrelation. λ is driven in part by local hydroclimate conditions and is generally larger than the 50‐mm nominal radiometric length scale for the soil moisture retrievals, suggesting broad subsurface correlation due to moisture drainage. In all but the most arid regions, radiometric soil moisture retrievals provide more information about ecosystem‐relevant water fluxes than satellite radiometers can explicitly “see”; lower‐frequency radiometers are expected to provide still more statistical information about subsurface water dynamics.
The relationship between evaporative fraction (EF) and soil moisture (SM) has traditionally been used in atmospheric and land‐surface modeling communities to determine the coupling strength between land surfaces and the atmosphere in the context of the dominant evaporation regime (energy or moisture limited). However, observation‐based analyses suggest that EF‐SM relationship in a given region can shift subject to other environmental factors, potentially influencing the determination of the dominant evaporation regime. This implies more complex dependencies embedded in the conventional EF‐SM relationship and that in fact it is a multidimensional function. In this study, we develop a generalized EF framework that explicitly accounts for dependencies on other environmental conditions. We show that large scatter in observed EF‐SM relationships is primarily due to the projection of variations in other dimensions and propose a normalization of the EF‐SM relationship accounting for the dimensions and dependencies not included in the conventional relationship. In this first study, we focus on bare soil conditions in order to establish the basic theoretical framework. The new generalized EF framework provides new insights into the origin of transition between energy‐limited and moisture‐limited evaporation regimes (marked by a critical SM), linked to soil type and meteorological input data (primarily wind speed and air temperature, but not solar radiation) dominating the evolution of land surface temperature and thus the relative efficiency of surface energy balance components during surface drying. Our results offer new opportunities to advance predictive capabilities quantifying land‐atmosphere coupling for a wide range of present and projected meteorological input data.
The degree to which individual pulses of available water drive plant activity across diverse biomes and climates is not well understood. It has previously only been investigated in a few dryland locations. Here, plant water uptake following pulses of surface soil moisture, an indicator for the pulse–reserve hypothesis, is investigated across South America, Africa and Australia with satellite-based estimates of surface soil and canopy water content. Our findings show that this behaviour is widespread: occurring over half of the vegetated landscapes. We estimate spatially varying soil moisture thresholds at which plant water uptake ceases, noting dependence on soil texture and proximity to the wilting point. The soil type and biome-dependent soil moisture threshold and the plant soil water uptake patterns at the scale of Earth system models allow a unique opportunity to test and improve model parameterization of vegetation function under water limitation.
Accurately characterizing evapotranspiration is critical when predicting the response of the hydrologic cycle to climate change. Although Earth system models estimate similar magnitudes of global evapotranspiration, the magnitude of each contributing source varies considerably between models due to the lack of evapotranspiration partitioning data. Here we develop an observation‐based method to partition evapotranspiration into soil evaporation and transpiration using meteorological data and satellite soil moisture retrievals. We apply the methodology at 1,614 weather stations across the continental United States during the summers of 2015 and 2016. We evaluate the method using vegetation indices inferred from satellites, finding strong spatial correlations between modeled transpiration and solar‐induced fluorescence (r2 = 0.87), and modeled vegetation fraction and leaf area index (r2 = 0.70). Since the sensitivity of evapotranspiration to environmental factors depends on the contribution of each source component, understanding the partitioning of evapotranspiration is increasingly important with climate change.
The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth dz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale dz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale dz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large dz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median dz across CONUS is 135 mm. The spatial variance of dz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.
This study presents an observation-driven technique to delineate the dominant boundaries and temporal shifts between different hydrologic regimes over the contiguous United States (CONUS). The energy- and water-limited evapotranspiration regimes as well as percolation to the subsurface are hydrologic processes that dominate the loss of stored water in the soil following precipitation events. Surface soil moisture estimates from the NASA Soil Moisture Active Passive (SMAP) mission, over three consecutive summer seasons, are used to estimate the soil water loss function. Based on analysis of the rates of soil moisture dry-downs, the loss function is the conditional expectation of negative increments in the soil moisture series conditioned on soil moisture itself. An unsupervised classification scheme (with cross validation) is then implemented to categorize regions according to their dominant hydrological regimes based on their estimated loss functions. An east–west divide in hydrologic regimes over CONUS is observed with large parts of the western United States exhibiting a strong water-limited evapotranspiration regime during most of the times. The U.S. Midwest and Great Plains show transitional behavior with both water- and energy-limited regimes present. Year-to-year shifts in hydrologic regimes are also observed along with regional anomalies due to moderate drought conditions or above-average precipitation. The approach is based on remotely sensed surface soil moisture (approximately top 5 cm) at a resolution of tens of kilometers in the presence of soil texture and land cover heterogeneity. The classification therefore only applies to landscape-scale effective conditions and does not directly account for deeper soil water storage.
Events of recent years—including extended droughts across California, record fires across western Canada, and destabilization of marine ecosystems—highlight the profound impact of multiannual to decadal‐scale climate shifts upon physical, biological, and socioeconomic systems. While previous research has focused on the influence of decadal‐scale climate oscillations such as the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation/Interdecadal Pacific Oscillation, recent research has revealed the presence of a quasi‐decadal mode of climate variability that, unlike the quasi‐stationary standing wave‐like structure of the oscillatory modes, involves a progression of atmospheric pressure anomalies around the North Pacific, which has been termed the Pacific Decadal Precession (PDP). In this paper we develop a set of methods to track the spatial and temporal evolutions of the PDP within historical observations as well as numerical model simulations. In addition, we provide a method that approximates the time evolution of the PDP across the full period of available data for real‐time monitoring of the PDP. Through the development of these tracking methods, we hope to provide the community with a consistent framework for future analysis and diagnosis of the PDP’s characteristics and underlying processes, thereby avoiding the use of different, and disparate, phenomenological‐ and mathematical‐based indices that can confound our understanding of the PDP and its evolution.
Loss terms in the land water budget (including drainage, runoff, and evapotranspiration) are encoded in the shape of soil moisture “drydowns”: the soil moisture time series directly following a precipitation event, during which the infiltration input is zero. The rate at which drydowns occur—here characterized by the exponential decay time scale τ—is directly related to the shape of the loss function and is a key characteristic of global weather and climate models. In this study, we use 1 year of surface soil moisture observations from NASA’s Soil Moisture Active Passive mission to characterize τ globally. Consistent with physical reasoning, the observations show that τ is lower in regions with sandier soils, and in regions that are more arid. To our knowledge, these are the first global estimates of τ—based on observations alone—at scales relevant to weather and climate models.
Our ability to predict precipitation on climate time-scales (months–decades) is limited by our ability to separate signals in the climate system (cyclical and secular) from noise — that is, variability due to processes that are inherently unpredictable on climate time-scales. This dissertation describes methods for characterizing “weather” noise — variability that arises from daily-scale processes — as well as the potential predictability of precipitation on climate time-scales. In each method, we make use of a climate-stationary null model for precipitation and determine which characteristics of the true, non-stationary system cannot be captured by a stationary assumption. This un-captured climate variability is potentially predictable, meaning that it is due to climate time-scale processes, although those processes themselves may not be entirely predictable, either practically or theoretically. The three primary methods proposed in this dissertation are 1. A stochastic framework for modeling precipitation occurrence with proper daily-scale memory representation, using variable order Markov chains and information criteria for order selection. 2. A corresponding method for representing precipitation intensity, allowing for memory in intensity processes. 3. A semi-parametric stochastic framework for precipitation which represents intensity and occurrence without separating the processes, designed to handle the issues that arise from estimating likelihoods for zero-inflated processes. Using each of these methods, potential predictability is determined across the contiguous 48 United States. Additionally, the methods of Chapter 4 are used to determine the magnitude of weather and climate variability for the “historical runs” of five global climate models for comparison against observational data. It is found that while some areas of the contiguous 48 United States are potentially very predictable (up to ∼ 70% of interannual variability), many regions are so dominated by weather noise that climate signals are effectively masked. Broadly, perhaps 20–30% of interannual variability may be potentially predictable, but this ranges considerably with geography and the annual seasonal cycle, yielding “hot spots” and “cold spots” of potential predictability. The analyzed global climate models demonstrate a fairly robust representation of weather-scale processes, and properly represent the ratio of weather-to- climate induced variability, despite some regional errors in mean precipitation totals and corresponding variability.
While low-frequency variations in precipitation amount, occurrence counts (hereafter “occurrence”), and intensity can take place on seasonal to multidecadal time scales, it is often unclear at which time scales these precipitation variations can be ascribed to potentially predictable, climate-induced changes versus simple, stochastic (i.e., random) precipitation event evolutions. This paper seeks to isolate the dominant time scales at which potentially predictable changes in observed precipitation characteristics occur over the continental United States and analyze sources of revealed potentially predictable precipitation variations for particular regions. The results highlight that at interannual time scales (here defined as those shorter than 7 years), the potential for predicting annual precipitation amounts tends to be higher than for annual event occurrence or intensity, with interannual potential predictability highest in both relatively dry and wet locations and lowest in transition regions. By contrast, at time scales greater than 7 years the potential for predicting annual event occurrence tends to be higher than amount or intensity, with >20-yr time scale potential predictability highest in relatively wet locations and lowest in relatively dry locations. To highlight the utility of this type of analysis, two robust signals are selected for further investigation, including 1) approximately 10-yr time scale variations in potentially predictable annual amounts over the northwestern United States and 2) 20–60-yr time scale variations in potentially predictable annual event occurrence over the southwestern United States. While mechanistic drivers for these observed variations are still being investigated, concurrent and precursor climate-state estimates in the atmosphere and ocean—principally over the Pacific sector—are provided, the monitoring of which may help realize the potential for predicting precipitation variations in these regions.
Sustained droughts over the Northwestern U.S. can alter water availability to the region’s agricultural, hydroelectric, and ecosystem service sectors. Here we analyze decadal variations in precipitation across this region and reveal their relation to the slow (~10 year) progression of an atmospheric pressure pattern around the North Pacific, which we term the Pacific Decadal Precession (PDP). Observations corroborate that leading patterns of atmospheric pressure variability over the North Pacific evolve in a manner consistent with the PDP and manifest as different phases in its evolution. Further analysis of the data indicates that low‐frequency fluctuations of the tropical Pacific Ocean state energize one phase of the PDP and possibly the other through coupling with the polar stratosphere. Evidence that many recent climate variations influencing the North Pacific/North American sector over the last few years are consistent with the current phase of the PDP confirms the need to enhance our predictive understanding of its behavior.
The goal of this paper is to detect secular trends in observed, station‐based precipitation variations and extreme event occurrences over the United States. By definition, detectable trends are those that are unlikely to have arisen from internal variability alone. To represent this internal variability, we use station‐specific, seasonally varying, daily time scale stationary stochastic weather models—models in which the simulated interannual‐to‐multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—to first estimate the trends in various means and extremes that can occur even with fixed, climatological daily precipitation characteristics. Detection of secular trends in the observed climate—whether naturally or anthropogenically induced—can then be defined relative to this stochastic variability, i.e., as trends in the means and/or extremes that are likely to have occurred only through a change in the underlying precipitation characteristics. The derived results have two important ramifications. First, they identify “hot spot” regions in which trends in precipitation characteristics are already emerging from within the envelope of stochastic variability, including (but not limited to) positive trends in annual occurrence across most of the U.S. and positive trends in annual intensity and heavy‐event accumulations across the Interior Plains and around the Great Lakes. Further, they identify “sentinel” metrics which show the greatest detectability—e.g., annual precipitation occurrence and intensity—as well as those which show the least detectability and hence are unlikely to produce any statistically meaningful signals—e.g., seasonal total precipitation and extremes.
The release of seasonal (and longer) predictions of various climatological quantities is now routine. While undoubtedly devastating to lives and livelihoods, it is unclear whether seasonal extremes in precipitation—for example, extreme dry spells leading to droughts or heavy precipitation events leading to flooding—represent a feasible target for these predictions, that is, whether they are potentially predictable or are instead inherently unpredictable more than a few days to weeks in advance. This paper assesses the potential for predicting seasonal extremes in observed precipitation as a function of region and time of year by decomposing the station-based variance into that attributable to short-memory behavior of typical meteorological events—as generated from station-specific, seasonally varying, daily time-scale stationary stochastic weather models (SSWMs)—and that attributable to longer-time-scale, potentially predictable changes in precipitation-producing processes. Findings suggest the potential for making skillful predictions of seasonal precipitation extremes over the United States is enhanced (reduced) during the cool (warm) season, particularly for heavy precipitation event accumulations. Further, this potential is accentuated along the West Coast, around the Great Lakes, and over the central plains and Ohio River valley but is diminished over the Northeast and northern Great Plains. However, findings also suggest the potential for producing seasonal (and longer) predictions of seasonal precipitation extremes is spatially and seasonally dependent. As such, this paper includes supplemental material for the potentially predictable variance of seasonal extreme dry spell lengths, heavy event accumulations, and total accumulations at 774 stations across all 365 days so readers can evaluate the potential predictability for the location, timing, and metric of most relevance to them.
Background and Aims: Many individual studies have shown that the timing of leaf senescence in boreal and temperate deciduous forests in the northern hemisphere is influenced by rising temperatures, but there is limited consensus on the magnitude, direction and spatial extent of this relationship.
Methods: A meta-analysis was conducted of published studies from the peer-reviewed literature that reported autumn senescence dates for deciduous trees in the northern hemisphere, encompassing 64 publications with observations ranging from 1931 to 2010.
Key Results: Among the meteorological measurements examined, October temperatures were the strongest predictors of date of senescence, followed by cooling degree-days, latitude, photoperiod and, lastly, total monthly precipitation, although the strength of the relationships differed between high- and low-latitude sites. Autumn leaf senescence has been significantly more delayed at low (25° to 49°N) than high (50° to 70°N) latitudes across the northern hemisphere, with senescence across high-latitude sites more sensitive to the effects of photoperiod and low-latitude sites more sensitive to the effects of temperature. Delays in leaf senescence over time were stronger in North America compared with Europe and Asia.
Conclusions: The results indicate that leaf senescence has been delayed over time and in response to temperature, although low-latitude sites show significantly stronger delays in senescence over time than high-latitude sites. While temperature alone may be a reasonable predictor of the date of leaf senescence when examining a broad suite of sites, it is important to consider that temperature-induced changes in senescence at high-latitude sites are likely to be constrained by the influence of photoperiod. Ecosystem-level differences in the mechanisms that control the timing of leaf senescence may affect both plant community interactions and ecosystem carbon storage as global temperatures increase over the next century.
Using weather station data, the parameters of a stationary stochastic weather model (SSWM) for daily precipitation over the contiguous United States are estimated. By construct, the model exactly captures the variance component of seasonal precipitation characteristics (intensity, occurrence, and total amount) arising from high-frequency variance. By comparing the variance of the lower-frequency accumulations (on the order of months) between the SSWM and the original measurements, potential predictability (PP) is estimated. Decomposing the variability into contributions from occurrence and intensity allows one to establish two contributing sources of total PP. Aggregated occurrence is found to have higher PP than either intensity or the seasonal total precipitation, and occurrence and intensity are found to interfere destructively when convolved into seasonal totals. It is recommended that efforts aimed at forecasting seasonal precipitation or attributing climate variability to particular processes should analyze occurrence and intensity separately to maximize signal-to-noise ratios. Significant geographical and seasonal variations exist in all PP components.
A generalizable method is presented for establishing the potential predictability for seasonal precipitation occurrence using rain gauge data. This method provides an observationally based upper limit for potential predictability for 774 weather stations in the contiguous United States. It is found that the potentially predictable fraction varies seasonally and spatially, and that on average 30% of year-to-year seasonal variability is potentially explained by predictable climate processes. Potential predictability is generally highest in winter, appears to be enhanced by orography and land surface coupling, and is lowest (stochastic variance is highest) along the Pacific coast. These results depict “hot” spots of climate variability, for use in guiding regional climate forecasting and in uncovering processes driving climate. Identified “cold” spots are equally useful in guiding future studies as predictable climate signals in these areas will likely be undetectable.
Water management, agriculture, and ecological systems are sensitive to the frequency and timing of precipitation. Here we document historical trends in these characteristics for station‐specific wet and dry seasons over the U.S. from 1930 to 2009. Simulations based on Markovian precipitation occurrence models are used as a null against which to test observed trends without resorting to area averaging. Most regions display increases in precipitation frequency during both wet and dry seasons accompanied by a decrease in length of dry spells. Prominent increases (decreases) occur over the Central and Great Plains during the dry season. An exception is the Atlantic Plains, which experienced a decrease in frequency and an increase in dry spell length, especially during the wet season. Regionally consistent trends in the timing of wet and dry seasons are also evident, particularly over the Ohio (Missouri) River valleys where the dry season now arrives up to 2–3 weeks earlier (later).