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P. C. D. Milly

Publications and source records attributed to P. C. D. Milly.

At least 19 recordsLinked to original sources
USGS2016

Climate variability and extremes, interacting with nitrogen storage, amplify eutrophication risk

Despite 30 years of basin-wide nutrient-reduction efforts, severe hypoxia continues to be observed in the Chesapeake Bay. Here we demonstrate the critical influence of climate variability, interacting with accumulated nitrogen (N) over multidecades, on Susquehanna River dissolved nitrogen (DN) loads, known precursors of the hypoxia in the Bay. We used the process model LM3-TAN (Terrestrial and Aquatic Nitrogen), which is capable of capturing both seasonal and decadal-to-century changes in vegetation-soil-river N storage, and produced nine scenarios of DN-load distributions under different short-term scenarios of climate variability and extremes. We illustrate that after 1 to 3 yearlong dry spells, the likelihood of exceeding a threshold DN load (56 kt yr −1 ) increases by 40 to 65% due to flushing of N accumulated throughout the dry spells and altered microbial processes. Our analyses suggest that possible future increases in climate variability/extremes—specifically, high precipitation occurring after multiyear dry spells—could likely lead to high DN-load anomalies and hypoxia.

Chesapeake Bay↗
USGS2012

GFDL's ESM2 global coupled climate-carbon Earth System Models. Part I: physical formulation and baseline simulation characteristics

We describe the physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory's previous CM2.1 climate model while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in the El Niño-Southern Oscillation being overly strong in ESM2M and overly weak ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to: total heat content variability given its lack of long term drift, gyre circulation and ventilation in the North Pacific, tropical Atlantic and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to: surface circulation given its superior surface temperature, salinity and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. Our overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords us the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.

Journal of Climate↗
USGS2011

On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

Earth Interactions↗
USGS2010

An analytic solution of the stochastic storage problem applicable to soil water

The accumulation of soil water during rainfall events and the subsequent depletion of soil water by evaporation between storms can be described, to first order, by simple accounting models. When the alternating supplies (precipitation) and demands (potential evaporation) are viewed as random variables, it follows that soil-water storage, evaporation, and runoff are also random variables. If the forcing (supply and demand) processes are stationary for a sufficiently long period of time, an asymptotic regime should eventually be reached where the probability distribution functions of storage, evaporation, and runoff are stationary and uniquely determined by the distribution functions of the forcing. Under the assumptions that the potential evaporation rate is constant, storm arrivals are Poisson-distributed, rainfall is instantaneous, and storm depth follows an exponential distribution, it is possible to derive the asymptotic distributions of storage, evaporation, and runoff analytically for a simple balance model. A particular result is that the fraction of rainfall converted to runoff is given by (1 - R −1 )/( e α(1− R −1) − R −1 ), in which R is the ratio of mean potential evaporation to mean rainfall and a is the ratio of soil water-holding capacity to mean storm depth. The problem considered here is analogous to the well-known problem of storage in a reservoir behind a dam, for which the present work offers a new solution for reservoirs of finite capacity. A simple application of the results of this analysis suggests that random, intraseasonal fluctuations of precipitation cannot by themselves explain the observed dependence of the annual water balance on annual totals of precipitation and potential evaporation.

Water Resources Research↗
USGS2010

Climate, soil water storage, and the average annual water balance

This paper describes the development and testing of the hypothesis that the long-term water balance is determined only by the local interaction of fluctuating water supply (precipitation) and demand (potential evapotranspiration), mediated by water storage in the soil. Adoption of this hypothesis, together with idealized representations of relevant input variabilities in time and space, yields a simple model of the water balance of a finite area having a uniform climate. The partitioning of average annual precipitation into evapotranspiration and runoff depends on seven dimensionless numbers: the ratio of average annual potential evapotranspiration to average annual precipitation (index of dryness); the ratio of the spatial average plant-available water-holding capacity of the soil to the annual average precipitation amount; the mean number of precipitation events per year; the shape parameter of the gamma distribution describing spatial variability of storage capacity; and simple measures of the seasonality of mean precipitation intensity, storm arrival rate, and potential evapotranspiration. The hypothesis is tested in an application of the model to the United States east of the Rocky Mountains, with no calibration. Study area averages of runoff and evapotranspiration, based on observations, are 263 mm and 728 mm, respectively; the model yields corresponding estimates of 250 mm and 741 mm, respectively, and explains 88% of the geographical variance of observed runoff within the study region. The differences between modeled and observed runoff can be explained by uncertainties in the model inputs and in the observed runoff. In the humid (index of dryness <1) parts of the study area, the dominant factor producing runoff is the excess of annual precipitation over annual potential evapotranspiration, but runoff caused by variability of supply and demand over time is also significant; in the arid (index of dryness >1) parts, all of the runoff is caused by variability of forcing over time. Contributions to model runoff attributable to small-scale spatial variability of storage capacity are insignificant throughout the study area. The consistency of the model with observational data is supportive of the supply-demand-storage hypothesis, which neglects infiltration excess runoff and other finite-permeability effects on the soil water balance.

Water Resources Research↗
USGS2007

Relating low‐flow characteristics to the base flow recession time constant at partial record stream gauges

Base flow recession information is helpful for regional estimation of low‐flow characteristics. However, analyses that exploit such information generally require a continuous record of streamflow at the estimation site to characterize base flow recession. Here we propose a simple method for characterizing base flow recession at low‐flow partial record stream gauges (i.e., sites with very few streamflow measurements under low‐streamflow conditions), and we use that characterization as the basis for a practical new approach to low‐flow regression. In a case study the introduction of a base flow recession time constant, estimated from a single pair of strategically timed streamflow measurements, approximately halves the root‐mean‐square estimation error relative to that of a conventional drainage area regression. Additional streamflow measurements can be used to reduce the error further.

Water Resources Research↗
USGS2007

Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method

To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis. ?? 2007 ASCE.

Journal of Hydrologic Engineering↗
USGS2006

GFDL's CM2 global coupled climate models. Part I: Formulation and simulation characteristics

The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved. Tw o versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2?? latitude ?? 2.5?? longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1?? in latitude and longitude, with meridional resolution equatorward of 30?? becoming progressively finer, such that the meridional resolution is 1/3?? at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments. The co ntrol simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic. Both models have be en used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/). ?? 2006 American Meteorological Society.

Journal of Climate↗
USGS2006

Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

Water Resources Research↗
USGS2005

Global pattern of trends in streamflow and water availability in a changing climate

Water availability on the continents is important for human health , economic activity , ecosystem function and geophysical processes . Because the saturation vapour pressure of water in air is highly sensitive to temperature, perturbations in the global water cycle are expected to accompany climate warming . Regional patterns of warming-induced changes in surface hydroclimate are complex and less certain than those in temperature, however, with both regional increases and decreases expected in precipitation and runoff. Here we show that an ensemble of 12 climate models exhibits qualitative and statistically significant skill in simulating observed regional patterns of twentieth-century multidecadal changes in streamflow. These models project 10–40% increases in runoff in eastern equatorial Africa, the La Plata basin and high-latitude North America and Eurasia, and 10–30% decreases in runoff in southern Africa, southern Europe, the Middle East and mid-latitude western North America by the year 2050. Such changes in sustainable water availability would have considerable regional-scale consequences for economies as well as ecosystems.

Nature↗
USGS2005

An analysis of region-of-influence methods for flood regionalization in the Gulf-Atlantic Rolling Plains

Region-of-influence (RoI) approaches for estimating stream flow characteristics at ungaged sites were applied and evaluated in a case study of the 50-year peak discharge in the Gulf-Atlantic Rolling Plains of the southeastern United States. Linear regression against basin characteristics was performed for each ungaged site considered based on data from a region of influence containing the n closest gages in predictor variable (PRoI) or geographic (GRoI) space. Augmentation of this count based cutoff by a distance based cutoff also was considered. Prediction errors were evaluated for an independent (split-sampled) dataset. For the dataset and metrics considered here: (1) for either PRoI or GRoI, optimal results were found when the simpler count based cutoff, rather than the distance augmented cutoff, was used; (2) GRoI produced lower error than PRoI when applied indiscriminately over the entire study region; (3) PRoI performance improved considerably when Rol was restricted to predefined geographic subregions.

Journal of the American Water Resources Associatio↗
USGS2005

Trends in the water budget of the Mississippi River basin, 1949-1997

This Fact Sheet is one in a series that highlights information or recent research findings from the USGS National Streamflow Information Program (NSIP). The investigations and scientific results reported in this series require a nationally consistent streamgaging network with stable long-term monitoring sites and a rigorous program of data quality assurance, management, archiving, and synthesis. NSIP produces multi-purpose, unbiased surface water information that is readily accessible to all.

Fact Sheet↗
USGS2004

The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations

The configuration and performance of a new global atmosphere and land model for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2-LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to El Nin??o-Southern Oscillation (ENSO). One AM2-LM2 integration was perfor med according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2-LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2-LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden-Julian oscillation. An ensemble of 10 AM2-LM 2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2-LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO. ?? 2004 American Meteorological Society.

Journal of Climate↗
USGS2003

Contribution of climate-driven change in continental water storage to recent sea-level rise

Using a global model of continental water balance, forced by interannual variations in precipitation and near-surface atmospheric temperature for the period 1981-1998, we estimate the sea-level changes associated with climate-driven changes in storage of water as snowpack, soil water, and ground water; storage in ice sheets and large lakes is not considered. The 1981-1998 trend is estimated to be 0.12 mm/yr, and substantial interannual fluctuations are inferred; for 1993-1998, the trend is 0.25 mm/yr. At the decadal time scale, the terrestrial contribution to eustatic (i.e., induced by mass exchange) sea-level rise is significantly smaller than the estimated steric (i.e., induced by density changes) trend for the same period, but is not negligibly small. In the model the sea-level rise is driven mainly by a downtrend in continental precipitation during the study period, which we believe was generated by natural variability in the climate system.

Proceedings of the National Academy of Sciences of↗
USGS2002

Increasing risk of great floods in a changing climate

Radiative effects of anthropogenic changes in atmospheric composition are expected to cause climate changes, in particular an intensification of the global water cycle with a consequent increase in flood risk. But the detection of anthropogenically forced changes in flooding is difficult because of the substantial natural variability; the dependence of streamflow trends on flow regime further complicates the issue. Here we investigate the changes in risk of great floods - that is, floods with discharges exceeding 100-year levels from basins larger than 200,000 km2 - using both streamflow measurements and numerical simulations of the anthropogenic climate change associated with greenhouse gases and direct radiative effects of sulphate aerosols. We find that the frequency of great floods increased substantially during the twentieth century. The recent emergence of a statistically significant positive trend in risk of great floods is consistent with results from the climate model, and the model suggests that the trend will continue.

Nature↗
USGS2002

Global modeling of land water and energy balances. Part II: Land-characteristic contributions to spatial variability

Land water and energy balances vary around the globe because of variations in amount and temporal distribution of water and energy supplies and because of variations in land characteristics. The former control (water and energy supplies) explains much more variance in water and energy balances than the latter (land characteristics). A largely untested hypothesis underlying most global models of land water and energy balance is the assumption that parameter values based on estimated geographic distributions of soil and vegetation characteristics improve the performance of the models relative to the use of globally constant land parameters. This hypothesis is tested here through an evaluation of the improvement in performance of one land model associated with the introduction of geographic information on land characteristics. The capability of the model to reproduce annual runoff ratios of large river basins, with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, is assessed. To allow a fair comparison, the model is calibrated in both cases by adjusting globally constant scale factors for snow-free albedo, non-water-stressed bulk stomatal resistance, and critical root density (which is used to determine effective root-zone depth). The test is made in stand-alone mode, that is, using prescribed radiative and atmospheric forcing. Model performance is evaluated by comparing modeled runoff ratios with observed runoff ratios for a set of basins where precipitation biases have been shown to be minimal. The withholding of information on global variations in these parameters leads to a significant degradation of the capability of the model to simulate the annual runoff ratio. An additional set of optimization experiments, in which the parameters are examined individually, reveals that the stomatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in stand-alone mode. Further single-parameter experiments with surface roughness length, available water capacity, thermal conductivity, and thermal diffusivity show very little sensitivity to estimated global variations in these parameters. Finally, it is found that even the constant-parameter model performance exceeds that of the Budyko and generalized Turc-Pike water-balance equations, suggesting that the model benefits also from information on the geographic variability of the temporal structure of forcing.

Journal of Hydrometeorology↗
USGS2002

Global modeling of land water and energy balances. Part III: Interannual variability

The Land Dynamics (LaD) model is tested by comparison with observations of interannual variations in discharge from 44 large river basins for which relatively accurate time series of monthly precipitation (a primary model input) have recently been computed. When results are pooled across all basins, the model explains 67% of the interannual variance of annual runoff ratio anomalies (i.e., anomalies of annual discharge volume, normalized by long-term mean precipitation volume). The new estimates of basin precipitation appear to offer an improvement over those from a state-of-the-art analysis of global precipitation (the Climate Prediction Center Merged Analysis of Precipitation, CMAP), judging from comparisons of parallel model runs and of analyses of precipitation-discharge correlations. When the new precipitation estimates are used, the performance of the LaD model is comparable to, but not significantly better than, that of a simple, semiempirical water-balance relation that uses only annual totals of surface net radiation and precipitation. This implies that the LaD simulations of interannual runoff variability do not benefit substantially from information on geographical variability of land parameters or seasonal structure of interannual variability of precipitation. The aforementioned analyses necessitated the development of a method for downscaling of long-term monthly precipitation data to the relatively short timescales necessary for running the model. The method merges the long-term data with a reference dataset of 1-yr duration, having high temporal resolution. The success of the method, for the model and data considered here, was demonstrated in a series of model-model comparisons and in the comparisons of modeled and observed interannual variations of basin discharge.

Journal of Hydrometeorology↗