Geophysical Fluid Dynamics Laboratory

About: Geophysical Fluid Dynamics Laboratory is a facility organization based out in Princeton, New Jersey, United States. It is known for research contribution in the topics: Climate model & Climate change. The organization has 525 authors who have published 2432 publications receiving 264545 citations. The organization is also known as: GFDL.

New Estimate of Annual Poleward Energy Transport by Northern Hemisphere Oceans

Abstract: Recent measurements of the earth's radiation budget from satellites, together with extensive atmospheric energy transport summaries based on rawinsonde data, allow a new estimate of the required poleward energy transport by Northern Hemisphere oceans for the mean annual case. In the region of maximum net northward energy transport (30–35N), the oceans transport 47% of the required energy (1.7×1022 cal year−1). At 20N, the peak ocean transport accounts for 74% at that latitude; for the region 0–70N the ocean contribution averages 40%.

Determining the tropopause height from gridded data

Abstract: [1] A method is presented to determine tropopause height from gridded temperature data with coarse vertical resolution. The algorithm uses a thermal definition of the tropopause, which is based on the concept of a “threshold lapse-rate”. Interpolation is performed to identify the pressure at which this threshold is reached and maintained for a prescribed vertical distance. The method is verified by comparing the heights calculated from analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the observed heights at individual radiosonde stations. RMS errors in the calculated tropopause heights are generally small. They range from 30–40 hPa in the extratropics to 10–20 hPa in the tropics. The largest deviations occur in the subtropics, where the tropopause has strong meridional gradients that are not adequately resolved by the input data.

Anthropogenic CO2 in the oceans estimated using transit time distributions

Abstract: The distribution of anthropogenic carbon (C ant ) in the oceans is estimated using the transit time distribution (TTD) method applied to global measurements of chlorofluorocarbon-12 (CFC12). Unlike most other inference methods, the TTD method does not assume a single ventilation time and avoids the large uncertainty incurred by attempts to correct for the large natural carbon background in dissolved inorganic carbon measurements. The highest concentrations and deepest penetration of anthropogenic carbon are found in the North Atlantic and Southern Oceans. The estimated total inventory in 1994 is 134 Pg-C. To evaluate uncertainties the TTD method is applied to output from an ocean general circulation model (OGCM) and compared the results to the directly simulated C ant . Outside of the Southern Ocean the predicted C ant closely matches the directly simulated distribution, but in the Southern Ocean the TTD concentrations are biased high due to the assumption of 'constant disequilibrium'. The net result is a TTD overestimate of the global inventory by about 20%. Accounting for this bias and other centred uncertainties, an inventory range of 94–121 Pg-C is obtained. This agrees with the inventory of Sabine et al., who applied the ΔC* method to the same data. There are, however, significant differences in the spatial distributions: The TTD estimates are smaller than ΔC* in the upper ocean and larger at depth, consistent with biases expected in ΔC* given its assumption of a single parcel ventilation time. DOI: 10.1111/j.1600-0889.2006.00222.x

Impacts on Ocean Heat from Transient Mesoscale Eddies in a Hierarchy of Climate Models

Abstract: The authors characterize impacts on heat in the ocean climate system from transient ocean mesoscale eddies. Their tool is a suite of centennial-scale 1990 radiatively forced numerical climate simulations from three GFDL coupled models comprising the Climate Model, version 2.0–Ocean (CM2-O), model suite. CM2-O models differ in their ocean resolution: CM2.6 uses a 0.1° ocean grid, CM2.5 uses an intermediate grid with 0.25° spacing, and CM2-1deg uses a nominal 1.0° grid.Analysis of the ocean heat budget reveals that mesoscale eddies act to transport heat upward in a manner that partially compensates (or offsets) for the downward heat transport from the time-mean currents. Stronger vertical eddy heat transport in CM2.6 relative to CM2.5 accounts for the significantly smaller temperature drift in CM2.6. The mesoscale eddy parameterization used in CM2-1deg also imparts an upward heat transport, yet it differs systematically from that found in CM2.6. This analysis points to the fundamental role that ocea...

How well can CMIP5 simulate precipitation and its controlling processes over tropical South America

Abstract: Underestimated rainfall over Amazonia was a common problem for the Coupled Model Intercomparison Project phase 3 (CMIP3) models. We investigate whether it still exists in the CMIP phase 5 (CMIP5) models and, if so, what causes these biases? Our evaluation of historical simulations shows that some models still underestimate rainfall over Amazonia. During the dry season, both convective and large-scale precipitation is underestimated in most models. GFDL-ESM2M and IPSL notably show more pentads with no rainfall. During the wet season, large-scale precipitation is still underestimated in most models. In the dry and transition seasons, models with more realistic moisture convergence and surface evapotranspiration generally have more realistic rainfall totals. In some models, overestimates of rainfall are associated with the adjacent tropical and eastern Pacific ITCZs. However, in other models, too much surface net radiation and a resultant high Bowen ratio appears to cause underestimates of rainfall. During the transition season, low pre-seasonal latent heat, high sensible flux, and a weaker influence of cold air incursions contribute to the dry bias. About half the models can capture, but overestimate, the influences of teleconnection. Based on a simple metric, HadGEM2-ES outperforms other models especially for surface conditions and atmospheric circulation. GFDL-ESM2M has the strongest dry bias presumably due to its overestimate of moisture divergence, induced by overestimated ITCZs in adjacent oceans, and reinforced by positive feedbacks between reduced cloudiness, high Bowen ratio and suppression of rainfall during the dry season, and too weak incursions of extratropical disturbances during the transition season.