scholarly journals The sensitivity of oceanic precipitation to sea surface temperature

2019 ◽  
Vol 19 (14) ◽  
pp. 9241-9252 ◽  
Author(s):  
Jörg Burdanowitz ◽  
Stefan A. Buehler ◽  
Stephan Bakan ◽  
Christian Klepp
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Vertical Velocity ◽  
Climate Models ◽  
Sea Surface ◽  
Precipitation Event ◽  
Temporal Sampling ◽  
Precipitation Measurement ◽  
Along Track ◽  
Oceanic Precipitation

Abstract. Our study forms the oceanic counterpart to numerous observational studies over land concerning the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1 min resolution. Second, we exploit the most recent European Reanalysis version 5 (ERA5) at hourly resolution on a 31 km grid. Matched with each other, ERA5 vertical velocity allows the constraint of the OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super Clausius–Clapeyron scaling with a SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles, while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for a decreasing precipitation event duration with increasing SST. ERA5 precipitation reaches a local minimum at about 26 ∘C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For the strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolutions in observations and climate models are key to understanding and predicting the sensitivity of oceanic precipitation extremes to a change in SST.

scholarly journals On the sensitivity of oceanic precipitation to sea surface temperature

2019 ◽  
Author(s):  
Jörg Burdanowitz ◽  
Stefan A. Buehler ◽  
Stephan Bakan ◽  
Christian Klepp
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Vertical Velocity ◽  
Climate Models ◽  
Sea Surface ◽  
Precipitation Event ◽  
Temporal Sampling ◽  
Precipitation Measurement ◽  
Oceanic Precipitation

Abstract. Our study forms the oceanic counterpart to numerous observational studies over land considering the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1-minute resolution. Second, we exploit the most recent European Re-Analysis version 5 (ERA5) at hourly resolution on 31 km grid. Matched with each other, ERA5 vertical velocity allows to constrain OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super-Clausius-Clapeyron scaling with SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for decreasing precipitation event duration with SST. ERA5 precipitation reaches a local minimum at about 26 °C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolution in observations as well as climate models is key to understand and predict the sensitivity of oceanic precipitation extremes to a change in SST.

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

scholarly journals On the Interpretation of the North Atlantic Averaged Sea Surface Temperature

2020 ◽  
Vol 33 (14) ◽  
pp. 6025-6045
Author(s):  
Jing Sun ◽  
Mojib Latif ◽  
Wonsun Park ◽  
Taewook Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Climate Models ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
The North ◽  
Sst Variability ◽  
The North Atlantic ◽  
Different Time Scales

AbstractThe North Atlantic (NA) basin-averaged sea surface temperature (NASST) is often used as an index to study climate variability in the NA sector. However, there is still some debate on what drives it. Based on observations and climate models, an analysis of the different influences on the NASST index and its low-pass filtered version, the Atlantic multidecadal oscillation (AMO) index, is provided. In particular, the relationships of the two indices with some of its mechanistic drivers including the Atlantic meridional overturning circulation (AMOC) are investigated. In observations, the NASST index accounts for significant SST variability over the tropical and subpolar NA. The NASST index is shown to lump together SST variability originating from different mechanisms operating on different time scales. The AMO index emphasizes the subpolar SST variability. In the climate models, the SST-anomaly pattern associated with the NASST index is similar. The AMO index, however, only represents pronounced SST variability over the extratropical NA, and this variability is significantly linked to the AMOC. There is a sensitivity of this linkage to the cold NA SST bias observed in many climate models. Models suffering from a large cold bias exhibit a relatively weak linkage between the AMOC and AMO and vice versa. Finally, the basin-averaged SST in its unfiltered form, which has been used to question a strong influence of ocean dynamics on NA SST variability, mixes together multiple types of variability occurring on different time scales and therefore underemphasizes the role of ocean dynamics in the multidecadal variability of NA SSTs.

scholarly journals Do Climate Models Capture the Tropical Influences on North Pacific Sea Surface Temperature Variability?

2011 ◽  
Vol 24 (23) ◽  
pp. 6203-6209 ◽  
Author(s):  
Fabian Lienert ◽  
John C. Fyfe ◽  
William J. Merryfield
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Climate Models ◽  
Southern Oscillation ◽  
Temperature Variability ◽  
Sea Surface ◽  
Model Errors ◽  
The North ◽  
The North Pacific

Abstract This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.

South Atlantic sea surface temperature anomalies and air-sea interactions: stochastic models

1994 ◽  
Vol 12 (9) ◽  
pp. 903-909
Author(s):  
S. G. Dobrovolski
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
South Atlantic ◽  
Entropy Method ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Leading Role ◽  
Sea Surface Temperature Anomalies ◽  
Dynamic Stochastic

Abstract. Data on the South Atlantic monthly sea surface temperature anomalies (SSTA) are analysed using the maximum-entropy method. It is shown that the Markov first-order process can describe, to a first approximation, SSTA series. The region of maximum SSTA values coincides with the zone of maximum residual white noise values (sub-Antarctic hydrological front). The theory of dynamic-stochastic climate models is applied to estimate the variability of South Atlantic SSTA and air-sea interactions. The Adem model is used as a deterministic block of the dynamic-stochastic model. Experiments show satisfactorily the SSTA intensification in the sub-Antarctic front zone, with appropriate standard deviations, and demonstrate the leading role of the abnormal drift currents in these processes.

scholarly journals The ISAR Instrument Uncertainty Model

2016 ◽  
Vol 33 (11) ◽  
pp. 2415-2433 ◽  
Author(s):  
Werenfrid Wimmer ◽  
Ian S. Robinson
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Retrieval Algorithm ◽  
Total Uncertainty ◽  
Sources Of Uncertainty ◽  
Uncertainty Model ◽  
The Individual ◽  
Along Track ◽  
Instrument Accuracy

AbstractMeasurements of sea surface temperature at the skin interface () made by an Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) have been used for a number of years to validate satellite sea surface temperature (SST), especially high-accuracy observations such as made by the Advanced Along-Track Scanning Radiometer (AATSR). The ISAR instrument accuracy for measuring is ±0.1 K (Donlon et al.), but to satisfy Quality Assurance Framework for Earth Observation (QA4EO) principles and metrological standards (Joint Committee for Guides in Metrology), an uncertainty model is required. To develop the ISAR uncertainty model, all sources of uncertainty in the instrument are analyzed and an uncertainty value is assigned to each component. Finally, the individual uncertainty components are propagated through the ISAR retrieval algorithm to estimate a total uncertainty for each measurement. The resulting ISAR uncertainty model applied to a 12-yr archive of measurements from the Bay of Biscay shows that 77.6% of the data are expected to be within ±0.1 K and a further 17.2% are within 0.2 K.

scholarly journals Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

2011 ◽  
Vol 24 (7) ◽  
pp. 1869-1877 ◽  
Author(s):  
Shahadat Chowdhury ◽  
Ashish Sharma
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Nearest Neighbor ◽  
Sea Surface ◽  
Base Case ◽  
Grid Cells ◽  
Combination Approach ◽  
Global Grid ◽  
Global Sea Surface Temperature

Abstract This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.

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