Sea Temperature Change as an Indicator of Global Change

2009 ◽  
pp. 337-347 ◽  
Author(s):  
Martin J Attrill
2021 ◽  
Vol 18 (6) ◽  
pp. 2075-2090
Author(s):  
Matthias Volk ◽  
Matthias Suter ◽  
Anne-Lena Wahl ◽  
Seraina Bassin

Abstract. Multiple global change drivers affect plant productivity of grasslands and thus ecosystem services like forage production and the soil carbon sink. Subalpine grasslands seem particularly affected and may serve as a proxy for the cold, continental grasslands of the Northern Hemisphere. Here, we conducted a 4-year field experiment (AlpGrass) with 216 turf monoliths, subjected to three global change drivers: warming, moisture, and N deposition. Monoliths from six different subalpine pastures were transplanted to a common location with six climate scenario sites (CSs). CSs were located along an altitudinal gradient from 2360 to 1680 m a.s.l., representing an April–October mean temperature change of −1.4 to +3.0 ∘C, compared to CSreference with no temperature change and with climate conditions comparable to the sites of origin. To uncouple temperature effects along the altitudinal gradient from soil moisture and soil fertility effects, an irrigation treatment (+12 %–21 % of ambient precipitation) and an N-deposition treatment (+3 kg and +15 kg N ha−1 a−1) were applied in a factorial design, the latter simulating a fertilizing air pollution effect. Moderate warming led to increased productivity. Across the 4-year experimental period, the mean annual yield peaked at intermediate CSs (+43 % at +0.7 ∘C and +44 % at +1.8 ∘C), coinciding with ca. 50 % of days with less than 40 % soil moisture during the growing season. The yield increase was smaller at the lowest, warmest CS (+3.0 ∘C) but was still 12 % larger than at CSreference. These yield differences among CSs were well explained by differences in soil moisture and received thermal energy. Irrigation had a significant effect on yield (+16 %–19 %) in dry years, whereas atmospheric N deposition did not result in a significant yield response. We conclude that productivity of semi-natural, highly diverse subalpine grassland will increase in the near future. Despite increasingly limiting soil water content, plant growth will respond positively to up to +1.8 ∘C warming during the growing period, corresponding to +1.3 ∘C annual mean warming.


2006 ◽  
Vol 23 (3) ◽  
pp. 487-500 ◽  
Author(s):  
Elizabeth C. Kent ◽  
Alexey Kaplan

Abstract A method is developed to quantify systematic errors in two types of sea surface temperature (SST) observations: bucket and engine-intake measurements. A simple linear model is proposed where the SST measured using a bucket is cooled or warmed by a fraction of the air–sea temperature difference and the SST measured using an engine intake has a constant bias. The model is applied to collocated nighttime observations made at moderate wind speeds, allowing the effects of solar radiation and strong vertical gradients in the upper ocean to be neglected. The analysis is complicated by large random errors in all of the variables used. To estimate coefficients in this model, a novel type of linear regression, where errors in two variables are correlated with each other, is introduced. Because of the uncertainty in a priori estimates of the error covariance matrix, a Bayesian analysis of the regression problem is developed, and maximum likelihood approximations to the posterior distributions of the model parameters are obtained. Results show that the temperature change in bucket SST resulting from the air–sea temperature difference can be detected. The analysis suggests that bucket SST may be in error by a fraction from 0.12° ± 0.02° to 0.16° ± 0.02°C of the air–sea temperature difference. When this temperature change of the bucket SST is accounted for, a warm bias in engine-intake SST in the mid- to late 1970s and the 1980s was found to be smaller than that suggested by previous studies, ranging between 0.09° ± 0.06° and 0.18° ± 0.05°C. For the early 1990s the model suggests that the engine-intake SSTs may have a cold bias of −0.13° ± 0.07°C.


2002 ◽  
Author(s):  
Trevor Hancock
Keyword(s):  

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