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The North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks., Author summary Mathematical models are now used to forecast infectious disease incidence at the population scale. By better understanding how errors in prediction systems are introduced, grow and impact the predictability of infectious disease, forecast accuracy could be improved. Here we explore the growth pattern of errors introduced from two major sources–model initial conditions and stochastic fluctuation–in a simple, compartmental model describing influenza transmission. We find that model initial error typically undergoes faster growth due to nonlinear amplification during model evolution. Adopting techniques used in numerical weather prediction, we leverage this growth of uncertainty and modify an ensemble forecast system to generate optimal perturbations along the fastest growing direction of initial error. This perturbation procedure increases ensemble spread, which better captures observations with large uncertainties. In retrospective forecasts for 95 US cities during the 2003 through 2014 flu seasons, this procedure leads to a substantial improvement of short-term forecast quality.

Snowmelt dominated streamflow of the Western Himalayan Rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in Northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in Northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper elevation stations and corresponding lower elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multi-collinearity of the predictor variables, cross-validated prediction skills of best subset were also compared with the prediction skills of Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra reservoir, a major energy and irrigation source in the region.

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth SystemModels must be coupled with Human SystemModels through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections.This makes current models likely to miss important feedbacks in the real Earth-Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models.The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth-Human system models for devising effective science-based policies and measures to benefit current and future generations.

A novel strategy is proposed for the efficient simulation of geochemical tracers in ocean models. The method captures the tracer advection and diffusion in a general circulation model (GCM) without any alteration (or even knowledge) of the GCM code. In comparison with offline tracer models, the proposed method is considerably more efficient and automatically includes all parameterizations of unresolved processes present in the most sophisticated GCMs. A comparison with a global configuration of the MIT GCM shows that the scheme can capture the complex three-dimensional transport of a state-of-the-art GCM. A key advantage of the proposed technique is the ability to directly compute steady-state solutions, a facility particularly well-suited to tracers such as natural radiocarbon. This capability is applied to develop a novel algorithm for accelerating the dynamical adjustment of ocean models. © 2004 Elsevier Ltd. All rights reserved.

Ocean circulation changes not needed What causes the pattern of sea surface temperature change that is seen in the North Atlantic Ocean? This naturally occurring quasi-cyclical variation, known as the Atlantic Multidecadal Oscillation (AMO), affects weather and climate. Some have suggested that the AMO is a consequence of variable large-scale ocean circulation. Clement et al. suggest otherwise. They find that the pattern of AMO variability can be produced in a model that does not include ocean circulation changes, but only the effects of changes in air temperatures and winds. Science , this issue p. 320

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill verification analyses may be applied to test the predictability of an empirical disease forecast model.

The last glacial period was marked by dramatic climate fluctuations. Sediment records from the Cariaco Basin and the Arabian Sea suggest that cooling in the North Atlantic region was tightly coupled with a southward displacement of the intertropical convergence zone and a weakening of the Indian summer monsoon. During the last glacial period, the North Atlantic regionexperienced pronounced, millennial-scale alternations between cold, stadial conditions and milder interstadial conditions—commonly referred to as Dansgaard–Oeschger oscillations—as well as periods of massive iceberg discharge known as Heinrich events1. Changes in Northern Hemisphere temperature, as recorded in Greenland2,3,4, are thought to have affected the location of the Atlantic intertropical convergence zone5,6 and the strength of the Indian summer monsoon7,8. Here we use high-resolution records of sediment colour—a measure of terrigenous versus biogenic content—from the Cariaco Basin off the coast of Venezuela and the Arabian Sea to assess teleconnections with the North Atlantic climate system during the last glacial period. The Cariaco record indicates that the intertropical convergence zone migrated seasonally over the site during mild stadial conditions, but was permanently displaced south of the basin during peak stadials and Heinrich events. In the Arabian Sea, we find evidence of a weak Indian summer monsoon during the stadial events. The tropical records show a more variable response to North Atlantic cooling than the Greenland temperature records. We therefore suggest that Greenland climate is especially sensitive to variations in the North Atlantic system—in particular sea-ice extent—whereas the intertropical convergence zone and Indian monsoon system respond primarily to variations in mean Northern Hemisphere temperature.

The impact of Arctic sea ice concentrations, surface albedo, cloud fraction, and cloud ice and liquid water paths on the surface shortwave (SW) radiation budget is analyzed in the twentieth-century simulations of three coupled models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The models are the Goddard Institute for Space Studies Model E-R (GISS-ER), the Met Office Third Hadley Centre Coupled Ocean–Atmosphere GCM (UKMO HadCM3), and the National Center for Atmosphere Research Community Climate System Model, version 3 (NCAR CCSM3). In agreement with observations, the models all have high Arctic mean cloud fractions in summer; however, large differences are found in the cloud ice and liquid water contents. The simulated Arctic clouds of CCSM3 have the highest liquid water content, greatly exceeding the values observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. Both GISS-ER and HadCM3 lack liquid water and have excessive ice amounts in Arctic clouds compared to SHEBA observations. In CCSM3, the high surface albedo and strong cloud SW radiative forcing both significantly decrease the amount of SW radiation absorbed by the Arctic Ocean surface during the summer. In the GISS-ER and HadCM3 models, the surface and cloud effects compensate one another: GISS-ER has both a higher summer surface albedo and a larger surface incoming SW flux when compared to HadCM3. Because of the differences in the models’ cloud and surface properties, the Arctic Ocean surface gains about 20% and 40% more solar energy during the melt period in the GISS-ER and HadCM3 models, respectively, compared to CCSM3. In twenty-first-century climate runs, discrepancies in the surface net SW flux partly explain the range in the models’ sea ice area changes. Substantial decrease in sea ice area simulated during the twenty-first century in CCSM3 is associated with a large drop in surface albedo that is only partly compensated by increased cloud SW forcing. In this model, an initially high cloud liquid water content reduces the effect of the increase in cloud fraction and cloud liquid water on the cloud optical thickness, limiting the ability of clouds to compensate for the large surface albedo decrease. In HadCM3 and GISS-ER, the compensation of the surface albedo and cloud SW forcing results in negligible changes in the net SW flux and is one of the factors explaining moderate future sea ice area trends. Thus, model representations of cloud properties for today’s climate determine the ability of clouds to compensate for the effect of surface albedo decrease on the future shortwave radiative budget of the Arctic Ocean and, as a consequence, the sea ice mass balance.

This paper briefly surveys areas of paleoclimate modeling notable for recent progress. New ideas, including hypotheses giving a pivotal role to sea ice, have revitalized the low-order models used to simulate the time evolution of glacial cycles through the Pleistocene, a prohibitive length of time for comprehensive general circulation models (GCMs). In a recent breakthrough, however, GCMs have succeeded in simulating the onset of glaciations. This occurs at times (most recently, 115 kyr b.p.) when high northern latitudes are cold enough to maintain a snow cover and tropical latitudes are warm, enhancing the moisture source. More generally, the improvement in models has allowed simulations of key periods such as the Last Glacial Maximum and the mid-Holocene that compare more favorably and in more detail with paleoproxy data. These models now simulate ENSO cycles, and some of them have been shown to reproduce the reduction of ENSO activity observed in the early to middle Holocene. Modeling studies have demonstrated that the reduction is a response to the altered orbital configuration at that time. An urgent challenge for paleoclimate modeling is to explain and to simulate the abrupt changes observed during glacial epochs (i.e., Dansgaard–Oescher cycles, Heinrich events, and the Younger Dryas). Efforts have begun to simulate the last millennium. Over this time the forcing due to orbital variations is less important than the radiance changes due to volcanic eruptions and variations in solar output. Simulations of these natural variations test the models relied on for future climate change projections. They provide better estimates of the internal and naturally forced variability at centennial time scales, elucidating how unusual the recent global temperature trends are.

Three sensitivity experiments using an Ocean Atmosphere General Circulation Model (OAGCM) are conducted to simulate the climate impact of precession. The relative contributions of components of the hydrological cycle including the albedo of Arctic sea ice, advection of atmospheric water vapor and sea surface temperature to the summer Arctic melt process are evaluated. Timing of the perihelion is varied in each experiment with meteorological spring (SP), winter (WP) and autumn (AP) perihelion corresponding to conditions at 110, 115 and 120 ky BP, respectively. Obliquity is unchanged at the 115 ky level which is lower than today. The experiments are assessed relative to the present day control, which has been shown to simulate current conditions based on observations. In the SP experiment, top of the atmosphere (TOA) insolation is weaker than today between the summer solstice and autumnal equinox. In the AP case representing the interglacial, it is less intense between vernal equinox and summer solstice but stronger during the remainder of the year. Although the incident solar radiation is reduced in summer in the SP experiment, increased melting of snow is found primarily as a result of feedbacks from the delayed seasonal cycle of hydrologic components. This is in contrast to both the WP and AP cases in which the perennial snow cover is simulated. At the time of the last glacial inception, 115 ky BP, the WP experiment shows lower insolation to the high northern latitudes in late spring and summer mainly as a result of lower obliquity than today. Dynamical ocean–atmosphere interactions in response to precession maintain the reduced sea ice melting in late spring, strengthen the annual equator-to-pole sea surface temperature (SST) gradient and increase atmospheric moisture convergence in glaciation-sensitive regions. In both the WP and

International audience; We investigate how well the 1975-1992 sea level interannual variability in the tropical Pacific is captured by dynamic height from temperature profiles. For each temperature profile, a surface dynamic height relative to 300 m is estimated, assuming a constant temperature-salinity relationship. After multiplication by a latitudinally varying factor and the removal of a seasonal cycle, the dynamic height deviations fit the tide gauge sea level variability to within the sampling errors, except at a few sites near the equator west of the date line, where surface salinity variability is large. The dynamic height data are assimilated into a wind-forced linear numerical model of the sea level in the tropical Pacific, applying a Kalman filter in a space of reduced dimension. A limited number of empirical orthogonal functions of the unfiltered run (1975-1992) define the reduced space, into which the Kalman Filter covariance evolution calculation is done [Cane et al., 1996]. Experiments indicate that results are better with 32 functions than with a smaller number but are not improved by retaining more functions. The resulting analyzed fields of sea level are compared to withheld dynamic height estimates from moorings, sea level data from tide gauges, and sea level analyses made with the same Kalman filter formalism applied to tide gauge measurements. The comparisons to observations suggest that the temperature profiles were usually sufficient to constrain the monthly analyzed fields to be close to the observed sea level with errors typically less than 3 cm near the equator. The comparison to tide gauge sea level reveals that this analysis is often more accurate than the analysis of tide gauge sea level data with which it shares many characteristics. Near the equator west of the date line, salinity variations are large and their neglect in estimating dynamic height has a negative impact on the analysis. The analyzed signal is underestimated in the southwest Pacific and at more than 20° off the equator. The reanalysis of the temperature data done with a primitive equation model at the National Meteorological Center (NMC) Ji et al., 1995; Enfield and Harris, 1995] does not share this problem. On the other hand, NMC reanalysis (RA4) departs more from the observations elsewhere, although more data were included than in our analysis.