As geleiras da Groenlândia podem estar derretendo 100 vezes mais rápido do que se pensava
Um modelo de computador foi criado por pesquisadores do Instituto Oden de Engenharia e Ciências Computacionais da Universidade do Texas em Austin, que determina a taxa na qual as frentes das geleiras da Groenlândia estão derretendo.
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)
Uma geleira derretendo na costa da Groenlândia. Crédito: Dr. Lorenz Meire, Greenland Climate Research Center.
Um modelo de computador foi criado por pesquisadores do Instituto Oden de Engenharia e Ciências Computacionais da Universidade do Texas em Austin, que determina a taxa na qual as frentes das geleiras da Groenlândia estão derretendo.
Publicado na revista Geophysical Research Letters , o modelo é o primeiro projetado especificamente para frentes verticais de geleiras – onde o gelo encontra o oceano em um ângulo agudo. Ele reflete observações recentes de uma frente de geleira do Alasca derretendo até 100 vezes mais rápido do que se supunha anteriormente. Segundo os pesquisadores, o modelo pode ser usado para melhorar tanto os modelos oceânicos quanto os das camadas de gelo, que são elementos cruciais de qualquer modelo climático global.
“Até agora, os modelos de derretimento da frente das geleiras foram baseados em resultados da Antártida, onde o sistema é bem diferente”, disse a principal autora Kirstin Schulz, pesquisadora associada do Grupo de Pesquisa Computacional em Sistemas de Gelo e Oceano (CRIOS) do Instituto Oden. . “Usando nosso modelo em um oceano ou modelo climático, podemos ter uma ideia muito melhor de como as frentes verticais das geleiras estão derretendo”.
O derretimento da camada de gelo da Groenlândia é um dos principais indicadores do aumento do nível do mar. Este trecho congelado de geleiras é o segundo maior da Terra e cobre cerca de 80% da nação nórdica. Se derreter totalmente, como aconteceu no auge do período interglacial Eemiano, cerca de 125.000 anos atrás, o nível global do mar pode subir 20 pés – ou aproximadamente 6,1 metros.
Durante décadas, o perigo da queda do gelo manteve os oceanógrafos longe dos penhascos irregulares das frentes das geleiras da Groenlândia, forçando-os a basear suas simulações nas plataformas de gelo estáveis ??que sustentam a Antártida.
“Durante anos, as pessoas pegaram o modelo de taxa de derretimento das geleiras flutuantes da Antártida e o aplicaram às frentes verticais das geleiras da Groenlândia”, disse Schulz. "Foi o melhor que pudemos fazer dadas as observações limitadas. Se estava certo ou errado, quem saberia? Mas há cada vez mais evidências de que a abordagem tradicional produz taxas de derretimento muito baixas nas frentes verticais das geleiras da Groenlândia."
Schulz e os coautores An T. Nguyen e Helen Pillar seguiram um caminho diferente. Ao projetar seu modelo, eles incorporaram a física única das frentes de geleiras da Groenlândia e forneceram dados obtidos mais perto de uma frente de geleira vertical do que nunca.
Há quatro anos, Rebecca Jackson, da Escola de Ciências Biológicas e Ambientais de Rutgers, enviou caiaques robóticos com sensores oceanográficos a 400 metros da geleira LeConte, no Alasca, onde as pessoas não ousam pisar. Seu conjunto de dados pintou uma imagem inesperada: a frente da geleira LeConte estava derretendo 100 vezes mais rápido do que os modelos de derretimento de geleiras existentes poderiam prever.
Com esse conjunto de dados em mãos, Schulz uniu forças com Nguyen e Pillar para desenvolver um modelo melhor. Levando em conta a inclinação íngreme em que as frentes das geleiras da Groenlândia atingem o oceano, eles consideraram um novo conjunto de equações para descrever a taxa de degelo.
“Os resultados do modelo climático oceânico são altamente relevantes para a humanidade prever tendências associadas às mudanças climáticas , então você realmente quer acertar”, disse Schulz. "Este foi um passo muito importante para melhorar os modelos climáticos ."
Mais informações: K. Schulz et al, Uma parametrização da taxa de derretimento aprimorada e restrita por observação para frentes verticais de gelo de geleiras marinhas terminantes, Cartas de pesquisa geofísica (2022). DOI: 10.1029/2022GL100654
Informações do periódico: Cartas de pesquisa geofísica